State determination apparatus, state determination method, and integrated circuit

ABSTRACT

Provided is a state determination apparatus that appropriately performs pattern classification processing and/or pattern determination processing even when a map generated by the SOM technique includes discontinuous image regions. In the state determination apparatus, the matching processing unit obtains adaptability data indicating a correlation degree between template data indicating a state and the SOM output data. The state determination unit obtains a state evaluation value based on an activity value obtained by the activity value obtaining unit and the adaptability value. The time series estimation unit determines a state of an input data based on the state evaluation value and state transition probability between states. This allows for appropriately performing pattern classification processing and/or pattern determination processing even when a map generated by the SOM technique includes discontinuous image regions.

This application claims priority to Japanese Patent Application No.2016-089034 filed on Apr. 27, 2016, the entire disclosure of which ishereby incorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure relates a technique that analyzes input datausing SOM (Self-organizing map) to determine a state (pattern)indicating the input data.

Description of the Background Art

Self-organizing map (hereinafter referred to as “SOM”) is achieved bymodeling the visual cortex in the cerebral cortex. In the SOM, mappinginput data into any-number-dimensional data by unsupervised machinelearning enables visualization of multiple dimensional data (e.g.,pattern classification). For example, the SOM maps high-dimensionalinput data into two-dimensional data, thereby enabling thehigh-dimensional input data to be projected in a second-dimensional map.Using the projected two-dimensional data (two-dimensional map) allowsfor performing classification processing of the input data (patternclassification processing). In the SOM, observing the projectedtwo-dimensional data (two-dimensional map) through the above-describedprocessing allows for visually and easily grasping a state forclassification of the input data; thus the SOM is used in variousfields.

For example, Patent Literature 1 (Japanese Unexamined Patent PublicationNo 2007-202964) discloses a technique of performing waveform analysisusing the SOM.

The technique disclosed by Patent Literature 1 creates a waveform mapusing the SOM such that each of regions representing a waveform patternforms a continuous region. As shown in FIG. 16 in Patent Literature 1,the technique of Patent Literature 1 creates a waveform map in whichcontinuous image regions listed below only exist (no split image regionsexist) and the continuous image regions are clearly arranged.

-   -   (1) Image region representing the waveform A+    -   (2) Image region representing the waveform A    -   (3) Image region representing the waveform B+    -   (4) Image region representing the waveform B    -   (5) Image region representing the waveform C+    -   (6) Image region representing the waveform C    -   (7) Image region representing the waveform D+    -   (8) Image region representing the waveform D

In the technique of Patent Literature 1, performing classificationprocessing using the above-described waveform map allows the inputwaveform data to be classified appropriately (it is possible todetermine that the input waveform data is similar to a waveformpattern).

However, the technique of Patent Literature 1 needs to generate thewaveform map using the SOM technique such that a region representing awaveform pattern forms a continuous region. Thus, it is difficult forthe technique of Patent Literature 1 to perform classificationprocessing or pattern determination processing using a waveform maphaving discontinuous image regions (e.g., split image regions). In otherwords, with the conventional technique, the existence of discontinuousimage regions (e.g., split image regions) in the map generated by theSOM technique makes it difficult to appropriately perform classificationprocessing or pattern determination processing.

To solve the above problems, it is an object of the present invention toprovide a state determination apparatus, a state determination methodand an integrated circuit that appropriately perform patternclassification processing and/or pattern determination processing evenwhen a map generated by the SOM technique includes discontinuous imageregions (e.g., split image regions).

SUMMARY

To solve the above problem, a first aspect of the present inventionprovides a state determination apparatus including feature vectorobtaining circuitry, normalization circuitry, mapping conversioncircuitry, matching processing circuitry, state determination circuitry,and time series estimation circuitry.

The feature vector obtaining circuitry is configured to obtain featurevector data from measured data obtained by measuring an event with anunknown state.

The normalization circuitry is configured to obtain a norm of thefeature vector data and obtain normalized feature vector data bynormalizing the feature vector data.

The mapping conversion circuitry is configured to obtain SOM output databy mapping the feature vector data into a space whose dimension differsfrom a dimension of the feature vector data.

The matching processing circuitry is configured to obtain adaptabilitydata indicating a correlation degree between template data indicating astate and the SOM output data obtained by the mapping conversioncircuitry.

The state determination circuitry is configured to obtain a stateevaluation value based on the adaptability data obtained by the matchingprocessing circuitry.

The time series estimation circuitry is configured to estimate a stateindicated by the measured data based on the state evaluation value andstate transition probability between states.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a state determination apparatus 1000according to a first embodiment.

FIG. 2 is a schematic diagram of a template data obtaining unit 4according to the first embodiment.

FIG. 3 is a schematic diagram of an activity value obtaining unit 5according to the first embodiment.

FIG. 4 is a schematic diagram of the activity value obtaining unit 5 inthe case of M=6.

FIG. 5 is a schematic diagram of a matching processing unit 7 accordingto the first embodiment.

FIG. 6 is a schematic diagram of a state determination unit 8 accordingto the first embodiment in the case of M=6.

FIG. 7 is a schematic diagram of a time series estimation unit 9according to the first embodiment in the case of M=6.

FIG. 8 is a diagram showing digital data D1 (time series data D1)obtained by a data input unit 1 and frequency-domain data D2 obtained byperforming frequency transform on the digital data D1.

FIG. 9 is a diagram describing SOM processing and schematically showingthe structure of an SOM.

FIG. 10 is a diagram showing an example of template data.

FIG. 11 is a flowchart showing Processing 1 (a threshold process for anoutput value).

FIG. 12 is a diagram showing a waveform of a signal D1 (waveform in astate of Walking) obtained by the data input unit 1, feature vector dataD2 that a feature vector obtaining unit 2 obtains from the signal D1,and SOM output data D_som(1) that a mapping conversion unit 6 obtainsfrom the normalized feature vector data D3.

FIG. 13 is a diagram showing a waveform of the signal D1 (waveform in astate of Running) obtained by the data input unit 1, feature vector dataD2 that the feature vector obtaining unit 2 obtains from the signal D1,and SOM output data D_som(2) that the mapping conversion unit 6 obtainsfrom the normalized feature vector data D3.

FIG. 14 is a graph in which values calculated using a probabilitydensity function f(x) are plotted assuming that norm data of the featurevector data D2 in State k (k is a natural number satisfying 1≤k≤6)follows the normal distribution.

FIG. 15 is a table showing state transition probabilities for States 1to 6.

FIG. 16 is a state transition diagram for States 1 to 6.

FIG. 17 is a state transition diagram for States 1 to 6.

FIG. 18 is a state transition diagram for States 1 to 6.

FIG. 19 is a state transition diagram for States 1 to 6.

FIG. 20 is a diagram describing particle filter processing.

FIG. 21 is a schematic diagram of a state determination apparatus 1000Aaccording to a first modification of the first embodiment.

FIG. 22 is a schematic diagram of a matching processing unit 7Aaccording to a first modification of the first embodiment.

FIG. 23 is a schematic diagram of a state determination unit 8Aaccording to a first modification of the first embodiment.

FIG. 24 is a schematic diagram of a state determination apparatus 1000Baccording to a second modification of the first embodiment.

FIG. 25 is a schematic diagram of a state determination unit 8Baccording to the second modification of the first embodiment.

FIG. 26 is a schematic diagram of an exemplary configuration with a CPUbus

DESCRIPTION OF THE PREFERRED EMBODIMENTS First Embodiment

A first embodiment will now be described with reference to the drawings.

1.1 Structure of State Determination Apparatus

FIG. 1 is a schematic diagram of a state determination apparatus 1000according to the first embodiment.

FIG. 2 is a schematic diagram of a template data obtaining unit 4according to the first embodiment.

FIG. 3 is a schematic diagram of an activity value obtaining unit 5according to the first embodiment.

FIG. 4 is a schematic diagram of the activity value obtaining unit 5 inthe case of M=6.

FIG. 5 is a schematic diagram of a matching processing unit 7 accordingto the first embodiment.

FIG. 6 is a schematic diagram of a state determination unit 8 accordingto the first embodiment in the case of M=6.

FIG. 7 is a schematic diagram of a time series estimation unit 9according to the first embodiment in the case of M=6.

As shown in FIG. 1 , the state determination apparatus 1000 includes adata input unit 1, a feature vector obtaining unit 2, selector SEL1, anormalization unit 3, a template data obtaining unit 4, an activityvalue obtaining unit 5, a mapping conversion unit 6, a matchingprocessing unit 7, a state determination unit 8, and a time seriesestimation unit 9.

The data input unit 1 receives an electric signal (sensor output signal)outputted from a sensor (e.g., an acceleration sensor) for example. Thedata input unit 1 converts the received electric signal into a digitalsignal, and then transmits the converted signal, as a signal D1, to thefeature vector obtaining unit 2.

The feature vector obtaining unit 2 receives the signal D1 transmittedfrom the data input unit 1. The feature vector obtaining unit 2, forexample, performs frequency transform on the received signal D1 toobtain a feature vector. The feature vector obtaining unit 2 thentransmits the obtained feature vector data to the normalization unit 3as a signal D2 (feature vector data D2).

The normalization unit 3 receives the signal D2 (feature vector data D2)transmitted from the feature vector obtaining unit 2. The normalizationunit 3 obtains a norm (norm data) Norm from the feature vector data D2and performs normalization processing on the feature vector data D2 toobtain a normalized feature vector data D3. The normalization unit 3then transmits the obtained norm data Norm and the normalized featurevector data D3, as vector data vec_D3, to the selector SEL1.

The selector SEL1 receives the vector data D3 transmitted from thenormalization unit 3. The selector SEL1 transmits the vector data D3, inaccordance with a selecting signal sel1, to (1) the template dataobtaining unit 4 or (2) the activity value obtaining unit 5 and themapping conversion unit 6. Note that the selecting signal sel 1 isgenerated by a controller (not shown) that controls the variousfunctional units of the state determination apparatus 1000.

As shown in FIG. 2 , the template data obtaining unit 4 includes a normobtaining unit 41, a norm data storage unit 42, an SOM processing unit43, an SOM data storage unit 44, a template data generation unit 45, anda template data storage unit 46.

The norm obtaining unit 41 receives the vector data vec_D3 transmittedfrom the selector SEL1. The norm obtaining unit 41 extracts the normdata Norm from the vector data vec_D3 and then stores the extracted normdata Norm in the norm data storage unit 42. The norm obtaining unit 41reads norm data stored in the norm data storage unit 42 and obtains,from the read norm data, an average value Ave(StateK) of the norm dataand a standard deviation σ(StateK) of the norm data for each stateStateK, which is a state in which the read norm data was obtained. Thenorm obtaining unit 41 transmits the obtained data to the activity valueobtaining unit 5 as vector data vec_Prb.

The norm data storage unit 42 stores the norm data transmitted from thenorm obtaining unit 41. The norm data storage unit 42 is achieved usingRAM (Random Access Memory), for example. The norm data storage unit 42also transmits norm data to the norm obtaining unit 41 in accordancewith a data read instruction requested by the norm obtaining unit 41.

The SOM processing unit 43 receives the vector data vec_D3 transmittedfrom the selector SEL1. The SOM processing unit 43 extracts normalizedfeature vector data D3 from the vector data vec_D3 and then performs SOMprocessing using the extracted normalized feature vector data D3.

(1) In a learning phase, the SOM processing unit 43 performs SOMprocessing using the normalized feature vector data D3 to determinecombining weights between nodes in the input layer and neurons in theoutput layer. After the completion of the above-describe processing, theSOM processing unit 43 transmits the determined combining weights datato the mapping conversion unit 6 as combining weights data wij_fixed.

(2) In a template creating phase, the SOM processing unit 43 performsSOM processing using the normalized feature vector data D3 and thentransmits the SOM output data to the SOM data storage unit 44 as dataD_som(t), which is SOM output data at current timing t).

The SOM data storage unit 44 stores the SOM output data D_som(t)transmitted from the SOM processing unit 43. The SOM data storage unit44 is achieved using RAM (Random Access Memory), for example.

The template data generation unit 45 reads a plurality of pieces of SOMoutput data D_som, such as N (N is a natural number) pieces of SOMoutput data, each of which is obtained at timings t to t+N−1, from theSOM data storage unit 44. Note that a plurality of pieces of SOM datathat the template data generation unit 45 reads from the SOM datastorage unit 44 are referred to as vector data vec_D_som. The templatedata generation unit 45 generates template data from the plurality ofSOM data read from the SOM data storage unit 44, and then transmits thegenerated template data to the template data storage unit 46 as templatedata Tmpl.

The template data storage unit 46 stores the template data Tmpltransmitted from the template data generation unit 45. The template datastorage unit 46 is achieved using RAM (Random Access Memory), forexample.

As shown in FIG. 3 , the activity value obtaining unit 5 includes a dataobtaining unit 51 for activity calculation, M (M is a natural number)activity calculation units, which are a first activity calculation unit521 to an M-th activity calculation unit 52M.

The data obtaining unit 51 for activity calculation receives the vectordata vec_Prb transmitted from the norm obtaining unit 41 of the templatedata obtaining unit 4. The data obtaining unit 51 for activitycalculation obtains, from the vector data vec_Prb, an average valueAve(StateK) and a standard deviation σ(StateK) of the norm data of thefeature vector data D2 for each state StateK. The data obtaining unit 51for activity calculation transmits the average value and standarddeviation of the norm data of the feature vector data D2 for the k-thstate (k is a natural number satisfying 1≤k≤M), as Ave(k) and σ(k)respectively, to the k-th activity calculation unit.

The first activity calculation unit 521 receives the norm data Normincluded in the vector data vec_D3 transmitted from the selector SEL1and the average value Ave(1) and the standard deviation σ(1) of the normdata of the feature vector data D2 in a first state transmitted from thedata obtaining unit 51 for activity calculation. The first activitycalculation unit 521 calculate an activity value D_act(1), which is anactivity value in the first state, based on the average value Ave(1) andthe standard deviation σ(1) of the norm data of the feature vector dataD2 in the first state, and then transmits the calculated activity valueD_act(1) to the state determination unit 8.

The second activity calculation unit 522 to the M-th activitycalculation unit 52M performs the same processing as described above.

The activity value obtaining unit 5 transmits the activity valueD_act(1) to D_act(M), which are obtained by the first activitycalculation unit 521 to the M-th activity calculation unit 52M, to thestate determination unit 8, as vector data vec_D_act.

For ease of explanation, a case of M=6 will be described below.

FIG. 4 is a schematic diagram of the activity value obtaining unit 5 inthe case of M=6.

The mapping conversion unit 6 receives the normalized feature vectordata D3 included in the vector data vec_D3 transmitted from the selectorSEL1. The mapping conversion unit 6 also receives the combining weightsdata wij_fixed between nodes in the input layer and neurons in theoutput layer, which is determined by the SOM processing unit 43. Themapping conversion unit 6 performs SOM processing on the normalizedfeature vector data D3 using the combining weights data wij_fixed toobtain SOM output data D_som. The mapping conversion unit 6 transmitsthe obtained SOM output data D_som to the matching processing unit 7.

The matching processing unit 7 receives the SOM output data D_somtransmitted from the mapping conversion unit 6. The matching processingunit 7 reads a plurality of pieces of template data Tmpl from thetemplate data storage unit 46.

Note that a plurality of pieces of SOM data that the matching processingunit 7 reads from the template data storage unit 46 are referred to asvector data vec_Tmpl.

The matching processing unit 7 performs matching processing using theSOM output data D_som and the plurality pieces of template data readfrom the template data storage unit 46 to obtain adaptability data D_ffor each template data.

The matching processing unit 7 transmits the obtained adaptability dataD_f for each template data to the state determination unit 8 as vectordata vec_D_f.

As shown in FIG. 5 , the matching processing unit 7 includes a TP dataobtaining unit 71, and M (M is a natural number) inner productcalculation units, or a first inner product calculation unit 721 to M-thinner product calculation unit 72M.

The TP data obtaining unit 71 reads a plurality of pieces of templatedata Tmpl (M pieces of template data Tmpl(1) to Tmpl(M)) from thetemplate data storage unit 46. The TP data obtaining unit 71 transmits Mpieces of template data Tmpl(1) to Tmpl(M) to the first inner productcalculation unit 721 to the M-th inner product calculation unit 72M,respectively.

The first inner product calculation unit 721 receives the SOM outputdata D_som transmitted from the mapping conversion unit 6 and thetemplate data Tmpl(1) transmitted from the TP data obtaining unit 71.The first inner product calculation unit 721 performs processing forcalculating an inner product using the SOM output data D_som and thetemplate data Tmpl(1). The first inner product calculation unit 721transmits a signal representing the calculated inner product value tothe state determination unit 8 as a signal D_f(1).

Note that the same processing as described above is performed in thesecond inner product calculation unit 722 to M-th inner productcalculation unit 72M.

In other words, a k-th (k is a natural number satisfying 1≤k≤M) innerproduct calculation unit 72 k receives the SOM data D_som transmittedfrom the mapping conversion unit 6 and the template data Tmpl(k)transmitted from the TP data obtaining unit 71. The k-th inner productcalculation unit 72 k performs processing for calculating an innerproduct using the SOM output data D_som and the template data Tmpl(k).The k-th inner product calculation unit 72 k transmits a signalrepresenting the calculated inner product value to the statedetermination unit 8 as a signal D_f(k).

Note that the signal D_f(1) to D_f(M), each of which is transmitted fromthe corresponding one of the first inner product calculation unit 721 tothe M-th inner product calculation unit 72M, are collectively referredto as vector data vec_Tmpl.

For ease of explanation, a case of M=6 will be described below.

As shown in FIG. 6 , the state determination unit 8 includes a firstdetermination unit 81 to a sixth determination unit 86.

The first determination unit 81 receives the inner product value D_f(1)(the signal D_f(1)) obtained by the first inner product calculation unit721 and the activity value D_act(1) obtained by the first activitycalculation unit 521. The first determination unit 81 obtains a stateevaluation value D_deci(1) from the inner product value D_f(1) and theactivity value D_act(1) and then transmits a signal representing theobtained state evaluation value, as a signal D_deci(1), to the timeseries estimation unit 9.

Note that the second to the sixth determination units 82 to 86 are thesame as the first determination unit 81.

More specifically, the k-th determination unit 8 k (k is a naturalnumber satisfying 1≤k≤6) receives the inner product value D_f(k) (thesignal D_f(k)) obtained by the k-th inner product calculation unit 72 kand the activity value D_act(k) obtained by the k-th activitycalculation unit 52 k. The k-th determination unit 8 k obtains a stateevaluation value D_deci(k) from the inner product value D_f(k) and theactivity value D_act(k) and then transmits a signal representing theobtained state evaluation value, as a signal D_deci(k), to the timeseries estimation unit 9.

Note that the signal D_deci(1) to D_deci(6), each of which istransmitted from the corresponding one of the first determination unit81 to the sixth determination unit 86, are collectively referred to asvector data vec_D_deci.

The case of M=6 has been described above. However, the present inventionshould not be limited to this case; M may be another number instead ofsix.

As shown in FIG. 7 , the time series estimation unit 9 includes a PFprocessing unit (particles filter processing unit) 91 and a stateestimation unit 92.

The PF processing unit 91 receives the vector data vec_D_decitransmitted from the state determination unit 8 and state transitionprobability data D_tr_prb transmitted from the control unit (not shown)for controlling functional units in the state determination apparatus1000. The PF processing unit 91 performs particle filter processing onthe vector data vec_D_deci to obtain particle filter output values(state estimation values) D_pf(1) to D_pf(6) for State 1 to State 6,respectively. The PF processing unit 91 then transmits the obtainedparticle filter output values (state estimation values) D_pf(1) toD_pf(6) for State 1 to State 6 to the state estimation unit 92.

The state estimation unit 92 receives the particle filter output values(state estimation values) D_pf(1) to D_pf(6) for State 1 to State 6transmitted from the PF processing unit 91. The state estimation unit 92estimates a state at the present timing t based on the particle filteroutput values (state estimation values) D_pf(1) to D_pf(6) for State 1to State 6, and then transmits data representing the result of theestimation, as data Dout, out of the state determination apparatus 1000.

1.2 Operation of State Determination Apparatus

The operation of the state determination apparatus 1000 with theabove-described structure will now be described.

In one example, a case in which an output from an acceleration sensor(not shown) for detecting a state of a person is inputted into the statedetermination apparatus 1000 will now be described. For ease ofexplanation, a case in which the state determination apparatus 1000determines one of the following six states of a person will now bedescribed.

-   -   (1) Sitting state (a state indicating that a person is sitting)    -   (2) Standing state (a state indicating that a person is        standing)    -   (3) Running state (a state indicating that a person is running)    -   (4) Walking state (a state indicating that a person is walking)    -   (5) Upstairs state (a state indicating that a person is going up        stairs)    -   (6) Downstairs state (a state indicating that a person is going        down stairs) For the operation of the state determination        apparatus 1000, (1) the operation in the learning phase, (2) the        operation of the template creating phase, and (3) the operation        of the state determination processing phase will now be        described separately.        1.2.1 Operation of Learning Phase

First, the operation of the learning phase in the state determinationapparatus 1000 will be described.

The output from the acceleration sensor (not shown) for detecting astate of a person is inputted, as a signal Din, to the data input unit1. Note that the acceleration sensor may be attached to a human body,for example.

The data input unit 1 samples (digitizes) the signal Din to obtaindiscrete data (digital data) thereof. The data input unit 1 thentransmits the obtained discrete data (digital data) to the featurevector obtaining unit 2 as the signal D1.

The feature vector obtaining unit 2 transforms the discrete data D1(digital data D1) obtained by the data input unit 1, for example, usingGabor-wavelet transform, into frequency-domain data to obtain themagnitude and phase of each frequency spectrum of the digital data D1.

The feature vector obtaining unit 2 then obtains, for example, data ofthe magnitude of the frequency spectrum included in a partial frequencyband as the feature vector data D2.

The transform to obtain frequency-domain data from the discrete data D1(digital data D1) should not be limited to Gabor-wavelet transform; forexample, the discrete Fourier transform, the fast Fourier transform, thewavelet transform and other transforms may be used.

FIG. 8 is a diagram showing digital data D1 (time series data D1)obtained by a data input unit 1 (the upper portion of FIG. 8 ) andfrequency-domain data D2 obtained by performing frequency transform onthe digital data D1 (the lower portion of FIG. 8 ).

In the case of FIG. 8 , the feature vector obtaining unit 2 obtains8-dimensional data, which is vector data consisting of eight parameters(eight frequency components included in a range from 0 to 5 Hz) as thefrequency-domain data D2.

For ease of explanation, a case in which the feature vector obtainingunit 2 obtains 8-dimensional data, which is vector data consisting ofeight parameters (eight frequency components included in a range from 0to 5 Hz) as the frequency-domain data D2 will now be described.

The feature vector data D2 obtained as described above is transmittedfrom the feature vector obtaining unit 2 to the normalization unit 3.

The normalization unit 3 obtains a norm (norm data) Norm from thefeature vector data D2 and performs normalization processing on thefeature vector data D2 to obtain normalized feature vector data D3.

More specifically, assuming that the feature vector data D2 is denotedas D2=(x1, x2, . . . , xn), the normalization unit 3 performs processingcorresponding to the following Formula to obtain norm data Norm:Norm=sqrt(x1{circumflex over ( )}2+x2{circumflex over ( )}2+ . . .+xn{circumflex over ( )}2)where sqrt(x) is a function that returns the square root of x.

The normalization unit 3 obtains the normalized feature vector data D3by dividing the feature vector data D2 by the norm data Norm. Morespecifically, the normalization unit 3 obtains the normalized featurevector data D3 whose norm is “1” by dividing each element of the featurevector data D2 (each element of the vector) by the norm data Norm.

The norm data Norm and the normalized feature vector data D3 through theabove-describe processing are transmitted from the normalization unit 3to the selector SEL1 as vector data vec_D3.

The controller (not shown) that controls the various functional units ofthe state determination apparatus 1000 generates a selecting signal sel1for selecting the terminal 0 of the selector SEL1, and then transmitsthe generated selecting signal sel1 to the selector SEL1.

The selector SEL1 transmits the vector data D3 to the template dataobtaining unit 4 in accordance with the selecting signal sel1.

The SOM processing unit 43 of the template data obtaining unit 4performs SOM processing using the feature vector data D3 included in thevector data D3 transmitted from the selector SEL1 to determine thecombining weights between nodes in the input layer and neurons in theoutput layer. This processing will be described with reference to FIG. 9.

FIG. 9 is a diagram describing SOM processing and schematically showingthe structure of an SOM. Note that i-axis and j-axis are set as shown inFIG. 9 to define a plane on which neurons of the output layer aredisposed.

Assume that input data is given as n-dimensional real number vec_x=(x₁,x₂, . . . , x_(n)) and the two-dimensional SOM has neurons disposed onm1×m2(=M1) lattice points. More specifically, as shown in FIG. 9 , theinput layer has n nodes and the output layer has neurons each disposedon one of m1×m2(=M1) lattice points.

Each of nodes (n input nodes) in the input layer for receiving the inputdata vec_x is connected to all neurons (all output neurons). The neuronu_(i,j), which is located at (i, j) in the two-dimensional latticearrangement of the output layer, has variable combining weight vectorvec_w_(ij)=(w_(ij_1), w_(ij_2), . . . , w_(ij_n)) for weighting andcombining each element of the input data vec_x. This combining weightvector is referred to as a reference vector.

Note that w_(ij_3) is a combining weight for the output node at acoordinate (i, j) and k-th input node.

The SOM processing unit 43 performs SOM processing through processesdescribed in “<<Step 1>>” and “<<Step 2>>” below.

<<Step 1: Initialization>>

The SOM processing unit 43 randomly sets (sets using random numbers)initial values for vec_w₁₁, vec_w₁₂, . . . , and vec_w_(mm) at timingt=0 where n is the number of input data vectors and T (≥n) is the numberfor iteration.

<<Step 2: Learning>>

The SOM processing unit 43 performs processing described below at timingt.

2A:

The SOM processing unit 43 calculates a Euclidean distance dis(vec_x(t),vec_w_(ij)(t−1)) between the input data vec_x(t), which is an input dataat timing t, and each of reference vectors vec_w_(ij)(t−1). Note thatdis(vec1, vec2) is a function for calculating a Euclidean distancebetween a vector vec1 and a vector vec2.

2B:

The SOM processing unit 43 determines a neuron u_(IJ) with the minimumEuclidean distance, and then sets the determined neuron as a winningneuron u_(IJ). Note that the neuron u_(IJ) is a neuron located at thecoordinate (I, J).

Alternatively, the SOM processing unit 43 may determine a neuron u_(IJ)with the maximum inner product value between the input data vec_x(t) attiming t and the reference vector vec_w_(ij)(t−1) at timing t−1, andthen set the determined neuron as a winning neuron u_(IJ).

2C:

The SOM processing unit 43 performs learning processing for thereference vector vec_w_(ij)(t), which is a reference vector at timing t,using the following formula:vec_w _(ij)(t)vec_w _(ij)(t−1)+h((i,j),(I,J),t)×(vec_x(t)−vec_w_(ij)(t−1))

Note that h is a monotonically decreasing function and hascharacteristics below.

(1) The function h is a monotonically decreasing function with respectto t, and converges zero as t approaches infinity.

(2) The function h is a monotonically decreasing function with respectthe Euclidean distance dis(vec_u_(ij), vec_u_(IJ)) between a latticepoint (i, j) and a lattice point (I, J). The monotonically decreasingrate (amount) of the function h becomes larger as t becomes larger. Notethat vec_u_(ij) is a vector from a point (e.g., the origin) to theposition of a neuron u_(ij) and vec_u_(IJ) is a vector from a point(e.g., the origin) to the position of a neuron u_(IJ).

Furthermore, the SOM processing unit 43 performs the same processing asdescribed above at timing t+1.

Note that the timing t+1 is timing at which the normalization unit 3obtains the normalized feature vector data D3 and the SOM processingunit 43 receives, next to timing t, the normalized feature vector dataD3 obtained by the normalization unit 3.

The SOM processing unit 43 repeatedly performs the above-describedprocessing until timing t=T.

The SOM processing unit 43 performs the above-described SOM processing,and then obtains the reference vector vec_w_(ij) which has been obtainedwhen the above-described SOM processing has been completed as adetermined reference vector vec_w_(ij_)fixed. The SOM processing unit 43then transmits the determined reference vector vec_w_(ij_)fixed to themapping conversion unit 6.

For ease of explanation, a case in which the input data vec_x, ornormalized feature vector data D3, consists of eight pieces of frequencycomponent data and the second-dimensional SOM has neurons each of whichis assigned to its corresponding one of 32×32 lattice points will now bedescribed as one example.

The state determination apparatus 1000 also performs norm dataprocessing in the learning phase.

More specifically, the norm obtaining unit 41 extracts norm data Normfrom the vector data vec_D3 transmitted from the selector SEL1 and thenstores the extracted norm data Norm in the norm data storage unit 42. Inother words, the norm obtaining unit 41 extracts norm data Norm from thevector data D3, which is obtained from data inputted into the statedetermination apparatus 1000 when a person continues to be in a state(e.g., when a person is walking), and then stores the extracted normdata Norm in the norm data storage unit 42.

The norm obtaining unit 41 reads norm data, which is stored in the normdata storage unit 42 when a person continues to be in a state (e.g.,when a person is walking), and obtains, from the read norm data, anaverage value Ave(StateK) of the norm data and a standard deviationσ(StateK) of the norm data for each state StateK, which is a state inwhich the read norm data was obtained. The norm obtaining unit 41transmits the obtained data to the activity value obtaining unit 5 asvector data vec_Prb.

One example of data obtained by the norm obtaining unit 41 through theabove-describe processing will be described below.

-   (1) Sitting state (State 1)    -   Ave(1)=0.082989    -   σ(1)=0.059778-   (2) Standing state (State 2)    -   Ave(2)=0.334777    -   σ(2)=0.224319-   (3) Running state (State 3)    -   Ave(3)=31.286555    -   σ(3)=2.630188-   (4) Walking state (State 4)    -   Ave(4)=12.395692    -   σ(4)=3.409114-   (5) UpStairs state (State 5)    -   Ave(5)=8.146392    -   σ(5)=1.912558-   (6) DownStairs state (State 6)    -   Ave(6)=25.002112    -   σ(6)=3.254508

A case in which the above-described data is processed will be describedbelow.

The above-describe data is transmitted from the norm obtaining unit 41to the activity value obtaining unit 5 as vector data vec_Prb.

1.2.2 Operation of Template Creating Phase

Next, the operation of the template creating phase in the statedetermination apparatus 1000 will be described.

In the template creating phase, the state determination apparatus 1000creates template data for (1) Sitting state (a state indicating that aperson is sitting), (2) Standing state (a state indicating that a personis standing), (3) Running state (a state indicating that a person isrunning), (4) Walking state (a state indicating that a person iswalking), (5) UpStairs state (a state indicating that a person is goingup stairs), and (6) DownStairs state (a state indicating that a personis going down stairs).

First, a case of creating a template for Walking state (a stateindicating that a person is walking) will be described.

In this case, the output from the acceleration sensor (not shown) whilea person is walking (continues to be in the Walking state) is inputtedinto the data input unit 1 as the signal Din.

The data input unit 1 samples (digitizes using AD conversion) the signalDin to obtain discrete data (digital data). The data input unit 1transmits the obtained discrete data (digital data) to the featurevector obtaining unit 2 as the signal D1.

The feature vector obtaining unit 2 transforms the discrete data D1(digital data D1) obtained by the data input unit 1, for example, usingGabor-wavelet transform into frequency-domain data to obtain themagnitude and phase of each frequency spectrum of the digital data D1.

The feature vector obtaining unit 2 then obtains, for example, data ofthe magnitude of the frequency spectrum included in a partial frequencyband as the feature vector data D2.

The feature vector data D2 obtained by the feature vector obtaining unit2 is transmitted from the feature vector obtaining unit 2 to thenormalization unit 3.

The normalization unit 3 obtains a norm (norm data) Norm from thefeature vector data D2 and performs normalization processing on thefeature vector data D2 to obtain normalized feature vector data D3.

The normalization unit 3 transmits the obtained norm data Norm andnormalized feature vector data D3 to the selector SEL1, as vector datavec_D3.

The controller (not shown) that controls the various functional units ofthe state determination apparatus 1000 generates a selecting signal sel1for selecting the terminal 0 of the selector SEL1, and then transmitsthe generated selecting signal sel1 to the selector SEL1.

The selector SEL1 transmits the vector data vec_D3 to the SOM processingunit 43 in accordance with the selecting signal sel1.

The SOM processing unit 43 extracts the normalized feature vector dataD3 from the vector data vec_D3 transmitted from the selector SEL1 andperforms SOM processing using the normalized feature vector data D3. Inthis case, the SOM processing uses, as combining weight vectors(reference vectors) vec_w_(ij), the combining weight vectors (referencevectors) vec_w_(ij_)fixed that are determined through theabove-described learning processing. The SOM processing unit 43 performsSOM processing on the normalized feature vector data D3 using thecombining weight vectors (reference vectors) vec_w_(ij_)fixed to obtainSOM data D_som(t), which is SOM data at timing t. The SOM processingunit 43 transmits the obtained SOM data D_som(t) to the SOM data storageunit 44.

The SOM data storage unit 44 stores the SOM output data D_som(t)transmitted from the SOM processing unit 43.

While a person is walking (continues to be in the Walking state) duringa period from the timing t=1 to the timing t=N, the state determinationapparatus 1000 performs the above-described processing. The SOMprocessing unit 43 then stores N pieces of SOM output data D_som(t)obtained during the period from the timing t=1 to t=N to the SOM datastorage unit 44.

The template data generation unit 45 reads N pieces of SOM output dataD_som(1) to D_som(N) obtained during the period from the timing t=1 tot=N (the period in which the person is walking (continues to be in theWalking state)) from the SOM data storage unit 44. Using the SOM outputdata D_som(1) to D_som(N), the template data generation unit 45calculates an average value of the output values at the same coordinateposition on the second-dimensional SOM (an average of values output fromthe same output node). Template data for the Walking state (a stateindicating that a person is walking) is created by setting thecalculated average values to values of their corresponding nodes.

More specifically, assuming that the feature vector data vec_x(t), whichis feature vector data at timing t, is defined by the followingformulae:vec_x(t)=(x ₁(t),x ₂(t), . . . , x _(N)1(t))

-   -   N1=8, and        the determined combining weight vector f_vec_w_(ij) is defined        by the following formulae:        f_vec_w _(ij)=(w _(ij_1) ,w _(ij_2) , . . . , w _(ij_N)1)    -   N1=8,        the template data is obtained by the following formula:

$\begin{matrix}{{Formula}\mspace{14mu} 1} & \; \\{{{D_{ij}(t)} = {\sum\limits_{k = 1}^{N\; 1}\left\{ {{x_{k}(t)} \times w_{ij\_ k}} \right\}}}{{N\; 1} = 8}} & (1)\end{matrix}$where D_(ij)(t) is the output value of a neuron (output node) located at(i, j) on the second-dimensional SOM at timing t.

Note that w_(ij_k) is a combining weight between the output node at thecoordinate (i, j) and the k-th input node.

The template data generation unit 45 obtains a template data valueg_(ij) for a node corresponding to a neuron (output node) located at (i,j) on the second-dimensional SOM through the processing corresponding tothe formula below.

$\begin{matrix}{{Formula}\mspace{14mu} 2} & \; \\{g_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{D_{ij}(t)}}}} & (2)\end{matrix}$

The template data generation unit 45 obtains second-dimensional templatedata by assigning the value g_(ij) calculated through the aboveprocessing to the node at the coordinate (i, j)

FIG. 10 is a diagram showing an example of template data.

More specifically, FIG. 10(a) is a diagram showing a template data forSitting state (a state indicating that a person is sitting (State 1)).FIG. 10(b) is a diagram showing a template data for Standing state (astate indicating that a person is standing (State 2)). FIG. 10(c) is adiagram showing a template data for Running state (a state indicatingthat a person is running (State 3)).

FIG. 10(d) is a diagram showing a template data for Walking state (astate indicating that a person is walking (State 4)). FIG. 10(e) is adiagram showing a template data for UpStairs state (a state indicatingthat a person is going up stairs (State 5)). FIG. 10(f) is a diagramshowing a template data for DownStairs state (a state indicating that aperson is going down stairs (State 6)).

For example, FIG. 10(d) a diagram showing the template data Tmpl(4)(two-dimensional data consisting of 32×32 pieces of data) obtained,through the above-described processing, from N (N is a natural number)pieces of SOM output data D_som(1) to D_som(N) obtained during theperiod from the timing t=1 to the timing t=N (the period during which aperson is walking (continues to be in Walking state)). In FIG. 10(d),the pixel value at the coordinate (i, j) of the template data Tmpl(4) isthe value go calculated through the above-described processing.

In the template data shown in FIG. 10 , values g_(ij) are normalized torange from 0 to 1 inclusive (0≤g_(ij)≤1). In the template data shown inFIG. 10 , the template data is represented as image data in which apixel of g_(ij)=0 is depicted as a pixel with a pixel value of 0, whichcorresponds to W 0% level, and a pixel of g_(ij)=1 is depicted as apixel with a pixel value of 1, which corresponds to W 100% level.

The template data Tmpl(4) for the Walking state (a state in which aperson is walking) obtained by the template data generation unit 45 asdescribed above is stored in the template data storage unit 46.

Processing for creating template data for the Running state (a state inwhich a person is running) is performed in the same manner as describedabove. The template data for the Running state is referred to astemplate data Tmpl(3).

More specifically, the output from the acceleration sensor (not shown)while a person is running (continues to be in the Running state) duringa period from the timing t=1 to the timing t=N is inputted into the datainput unit 1 as the signal Din. The state determination apparatus 1000performs processing similar to the above-described processing to obtaintemplate data Tmpl(3) for the Running state (a state in which a personis running). The obtained template data Tmpl(3) for the Running state (astate in which a person is running) is then stored in the template datastorage unit 46.

FIG. 10(c) is a diagram showing the template data Tmpl(3)(two-dimensional data consisting of 32×32 pieces of data) obtained,through the above-described processing, from N (N is a natural number)pieces of SOM output data D_som(1) to D_som(N) obtained during theperiod from the timing t=1 to the timing t=N (the period during which aperson is running (continues to be in Running state)).

Processing for creating template data for other states is performed inthe same manner as described above.

A case in which the template data generation unit 45 creates templatedata by calculating an average value of the output values at the samecoordinate position on the second-dimensional SOM (an average of valuesoutput from the same output node) using the SOM output data D_som(1) toD_som(N) is described above. However, the present invention should notbe limited to the above-described processing. For example, the templatedata generation unit 45 may create template data through processingbelow (Processing 1 to Processing 4).

(1) Processing 1 (Threshold Processing for Output Values)

Using the SOM output data D_som(1) to D_som(N), the template datageneration unit 45 compares the output values D_(ij)(t) at the samecoordinate position on the second-dimensional SOM (the values outputfrom the same output node) with a threshold value Th1. The template datageneration unit 45 calculates an average value of the output valuesD_(ij)(t) satisfying D_(ij)(t)≥Th1, and then sets the calculated averagevalue as a value g_(ij) at the coordinate (i, j) of the template data.

This processing will be described with the flowchart shown in FIG. 11 .

In step S1, the template data generation unit 45 sets a variable trepresenting timing (time), a counter value Cnt, and a sum value Sum tozeros individually.

In step S2, the template data generation unit 45 increments the variablerepresent timing by one.

The template data generation unit 45 compares the output value D_(ij)(t)at the same coordinate position on the second-dimensional SOM (anaverage of values output from the same output node) with a thresholdvalue Th1. If D_(ij)(t)≥Th1 is satisfied (“Yes” at step S3), then theoutput value D_(ij)(t) is added to the sum value Sum (step S4), andfurthermore the counter value Cnt is incremented by one (step S5).

In contrast, if D_(ij)(t)≥Th1 is not satisfied (“No” at step S3), thenthe template data generation unit 45 advances the processing to step S6.

In step S6, if t≥N is not satisfied, then the template data generationunit 45 returns the processing to step S2. If t≥N is satisfied, then thetemplate data generation unit 45 substitutes the counter value Cnt intoa final counter value N1 (Step S7). Furthermore, the template datageneration unit 45 obtains the template value g_(ij) at the coordinate(i, j) through processing that corresponds to g_(ij)=Sum/N1 (Step S8).

The above-described processing allows an average value obtained byexcluding output values of SOM which are smaller than the thresholdvalue to be set as the template data value g_(ij), thereby creating moreappropriate template data.

(2) Processing 2 (Threshold Processing for Variance of Output Values)

Using the SOM output data D_som(1) to D_som(N), the template datageneration unit 45 calculates variance of the output values D_(ij)(t) atthe same coordinate position on the second-dimensional SOM (the valuesoutput from the same output node) through processing which correspondsto the following formulae:

$\begin{matrix}{{Formula}\mspace{14mu} 3} & \; \\{{AveD}_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{D_{ij}(t)}}}} & (3) \\{{Formula}\mspace{14mu} 4} & \; \\{v_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}\left( {{D_{ij}(t)} - {AveD}_{ij}} \right)^{2}}}} & (4)\end{matrix}$

The template data generation unit 45 then compares the variance v_(ij)with a threshold value Th2. Subsequently, the template data generationunit 45 calculates an average value of the SOM output values D_(ij)(t)at the coordinate (i, j) which satisfy v_(ij)≤Th2, and then sets thecalculated average value as a template data value g_(ij) at thecoordinate (i, j).

Note that if v_(ij)≤Th2 is not satisfied, the template data generationunit 45 sets template data values g_(ij) at the coordinate (i, j) tozeros.

Such processing allows an average value obtained using only SOM outputvalues D_(ij)(t) whose variance in a temporal direction is small to beset as a template data value g_(ij) at the coordinate (i, j), therebycreating more appropriate template data. In other words, theabove-described processing allows an average value obtained using onlySOM output values D_(ij)(t) less variable in the temporal direction tobe set as a template data value g_(ij) at the coordinate (i, j), therebycreating more appropriate template data.

(3) Processing 3 (Processing combining Processing 1 and Processing 2)

The template data generation unit 45 may obtain the template data valuesg_(ij) at the coordinate (i, j) through processing achieved by combiningthe above-described Processing 1 and Processing 2.

The template data generation unit 45 compares the variance v_(ij) withthe threshold Th2, calculates an average value using only SOM outputvalues D_(ij)(t) which satisfy v_(ij)≤Th2 and are larger than thethreshold value Th1, and then sets the calculated average value as atemplate data value go at the coordinate (i, j).

Note that if v_(ij)≤Th2 is not satisfied, the template data generationunit 45 sets a template data value g_(ij) at the coordinate (i, j) tozero.

As described above, processing achieved by combining Processing 1 andProcessing 2 allows for creating more appropriate template data.

(4) Processing 4 (Processing Using Data within a Range Defined by n1×σ)

The template data generation unit 45 performs, using the variancev_(ij), processing which corresponds to the formula below to obtain astandard deviation σ_(ij) of the SOM output values D_(ij)(t) at thecoordinate (i, j).σ_(ij)=sqrt(v _(ij))where sqrt(x) is a function that returns the square root of x.

If abs(D_(ij)(t)−AveD_(ij))≤n1×σ_(ij) (n1 is a positive natural number.abs(x) is a function that returns an absolute value of “x”) issatisfied, the template data generation unit 45 calculates an averagevalue of the SOM output values D_(ij)(t) at the coordinate (i, j) andthen sets the calculated average values at the coordinate (i, j) to atemplate data value g_(ij).

If abs(D_(ij)(t)−AveD_(ij))≤n1×σ_(ij) (n1 is a positive natural number.abs(x) is a function that returns an absolute value of “x”) is notsatisfied, the template data generation unit 45 sets a template datavalue go to zero.

Such processing allows for creating template data using data within arange from the average value minus n1×σ to the average value plus n1×σinclusive.

For example, when n1=3, performing processing as described above allowsfor creating template data using data within a range from the averagevalue minus 3σ to the average value plus 3σ inclusive. In the statedetermination apparatus 1000, assuming that SOM output values D_(ij)(t)follows the normal distribution, setting n1 to three (i.e., n1=3) allowsfor creating template data using data within a range occupying 99.7percent of the entire range. As described above, adjusting the value ofn1 in the state determination apparatus 1000 allows the range of the SOMoutput value D_(ij)(t) used for creating template data to be adjusted.

Through the above-described processing, the state determinationapparatus 1000 creates template data.

Note that normalization may be performed such that the sum of all thetemplate data values g_(ij) becomes “1”. Such normalization allowspossible range of values indicating correlation degree (e.g., innerproduct values or Euclidean distances) to be constant (common)regardless of templates to be used in the determination processing fordetermining the correlation degree between template data and SOM outputdata. This allows classification determination processing to be easilyachieved.

1.2.3 Operation of State Determination Processing Phase

Next, the operation of the state determination processing phase in thestate determination apparatus 1000 will be described.

FIG. 12 is a diagram showing a waveform of the signal D1 (waveform in astate of Walking) obtained by the data input unit 1 (the upper portionof FIG. 12 ), feature vector data D2 that the feature vector obtainingunit 2 obtains from the signal D1 (the left lower portion of FIG. 12 ),and SOM output data D_som(1) that the mapping conversion unit 6 obtainsfrom the normalized feature vector data D3 (the right lower portion ofFIG. 12 ).

FIG. 13 is a diagram showing a waveform of the signal D1 (waveform in astate of Running) obtained by the data input unit 1 (the upper portionof FIG. 13 ), feature vector data D2 that the feature vector obtainingunit 2 obtains from the signal D1 (the left lower portion of FIG. 13 ),and SOM output data D_som(2) that the mapping conversion unit 6 obtainsfrom the normalized feature vector data D3 (the right lower portion ofFIG. 13 ).

The output from the acceleration sensor (not shown) for detecting astate of a person is inputted, as a signal Din, to the data input unit1. Note that the acceleration sensor may be attached to a human body,for example.

The data input unit 1 samples (digitizes) the signal Din to obtaindiscrete data (digital data) thereof. The data input unit 1 thentransmits the obtained discrete data (digital data) to the featurevector obtaining unit 2 as the signal D1.

In Walking state, the signal D1 shown in the upper portion of FIG. 12 isobtained, for example. In Running state, the signal D1 shown in theupper portion of FIG. 13 is obtained.

The feature vector obtaining unit 2 obtains feature vector data D2 fromthe discrete data D1 (digital data D1) obtained by the data input unit 1in the same manner as processing in the learning phase.

In Walking state, the feature vector data D2 shown in the lower portionof FIG. 12 is obtained, for example. In Running state, the featurevector data D2 shown in the lower portion of FIG. 13 is obtained.

The feature vector data D2 obtained by the feature vector obtaining unit2 is transmitted from the feature vector obtaining unit 2 to thenormalization unit 3.

The normalization unit 3 obtains norm data Norm and normalized featurevector data D3 from the feature vector data D2 in the same manner asprocessing in the learning phase. The normalization unit 3 transmits theobtained data to the selector SEL1 as vector data vec_D3.

Assume that norm data Norm obtained in the case of FIG. 12 is “11.5” andnorm data Norm obtained in the case of FIG. 13 is “31.0”.

The controller (not shown) that controls the various functional units ofthe state determination apparatus 1000 generates a selecting signal sel1for selecting the terminal 1 of the selector SEL1, and then transmitsthe generated selecting signal sel1 to the selector SEL1.

The selector SEL1 transmits the vector data D3 to the activity valueobtaining unit 5 and the mapping conversion unit 6 in accordance withthe selecting signal sel1.

The first activity calculation unit 521 of the activity value obtainingunit 5 receives the norm data Norm transmitted included in the vectordata vec_D3 transmitted from the selector SEL1 and the average valueAve(1) and the standard deviation σ(1) of the norm data of the featurevector data D2 in State 1 (Sitting state) transmitted from the dataobtaining unit 51 for activity calculation. The first activitycalculation unit 521 calculate an activity value D_act(1), which is anactivity value in State 1, based on the average value Ave(1) and thestandard deviation σ(1) of the norm data of the feature vector data D2in State 1.

More specifically, assuming that the norm data of the feature vectordata D2 in State 1 follows the normal distribution, the activity valueobtaining unit 5 obtains the activity value D_act(1) using a probabilitydensity function f(x) following the normal distribution. In other words,the activity value obtaining unit 5 obtains the activity value D_act(1)through processing corresponding to the following formula:

$\begin{matrix}{{Formula}5} & \end{matrix}$ $\begin{matrix}{{f(x)} = {\frac{1}{\sqrt{2{\pi\sigma}^{2}}}{\exp\left( {- \frac{\left( {x - \mu} \right)^{2}}{2\sigma^{2}}} \right)}}} & (5)\end{matrix}$where μ is an average value, and σ is a standard deviation.

In the present embodiment, the average value Ave(1) and standarddeviation σ(1) of the norm data of the feature vector data D2 in State 1is as follows:

-   -   Ave(1)=0.082989    -   σ(1)=0.059778

Thus, the activity value obtaining unit 5 calculates the value of theprobability density function f(x) using μ=Ave(1)=0.082989 andσ=σ(1)=0.059778, and then obtains the calculated value as the activityvalue D_act(1).

The activity value obtaining unit 5 transmits the activity valueD_act(1) obtained through the above-describe processing to the statedetermination unit 8.

The second activity calculation unit 522 obtains an activity valueD_act(2) for State 2 (Standing state) through the same processing as theabove-describe processing.

More specifically, the second activity calculation unit 522 calculatesthe value of the probability density function f(x) usingμ=Ave(2)=0.334777 and σ=σ(2)=0.224319, and then obtains the calculatedvalue as the activity value D_act(2).

The third activity calculation unit 523 obtains an activity valueD_act(3) for State 3 (Running state) through the same processing as theabove-describe processing.

More specifically, the third activity calculation unit 523 calculatesthe value of the probability density function f(x) usingμ=Ave(3)=31.286555 and σ=σ(3)=2.630188, and then obtains the calculatedvalue as the activity value D_act(3).

The fourth activity calculation unit 524 obtains an activity valueD_act(4) for State 4 (Walking state) through the same processing as theabove-describe processing.

More specifically, the fourth activity calculation unit 524 calculatesthe value of the probability density function f(x) usingμ=Ave(4)=12.395692 and σ=σ(4)=3.409114, and then obtains the calculatedvalue as the activity value D_act(4).

The fifth activity calculation unit 525 obtains an activity valueD_act(5) for State 5 (UpStairs state) through the same processing as theabove-describe processing.

More specifically, the fifth activity calculation unit 525 calculatesthe value of the probability density function f(x) usingμ=Ave(5)=8.146392 and σ=σ(5)=1.912558, and then obtains the calculatedvalue as the activity value D_act(5).

The sixth activity calculation unit 526 obtains an activity valueD_act(6) for State 6 (DownStairs state) through the same processing asthe above-describe processing.

More specifically, the sixth activity calculation unit 526 calculatesthe value of the probability density function f(x) usingμ=Ave(6)=25.002112 and σ=σ(6)=3.254508, and then obtains the calculatedvalue as the activity value D_act(6).

FIG. 14 is a graph in which values calculated using a probabilitydensity function f(x) are plotted assuming that norm data of the featurevector data D2 in State k (k is a natural number satisfying 1≤k≤6)follows the normal distribution. In other words, FIG. 14 shows a graphof the probability density function f(x) in the case in which thevariable x of the function f(x) following the normal distribution is thenorm data Norm and the average value and standard deviation in State kare the above-describe values.

In one example, the activity values D_act(k) obtained by the activityvalue obtaining unit 5 in a case of x=Norm=1.5 (a case of FIG. 12 ) anda case of x=Norm=31.0 (a case of FIG. 13 ) will be shown below.

(1) In the case of x=Norm=11.5 (FIG. 12 )

-   -   D_act(1)=0.00    -   D_act(2)=0.00    -   D_act(3)=0.00    -   D_act(4)=0.11    -   D_act(5)=0.04    -   D_act(4)=0.00

In this case, the activity value D_act(4) for State 4 (Walking state) islarge.

(2) In the case of x=Norm=31.0 (FIG. 13 )

-   -   D_act(1)=0.00    -   D_act(2)=0.00    -   D_act(3)=0.15    -   D_act(4)=0.00    -   D_act(5)=0.00    -   D_act(4)=0.02

In this case, the activity value D_act(3) for State 3 (Running state) islarge.

The activity values D_act(1) to D_act(6) obtained for each state aretransmitted from the activity value obtaining unit 5 to the statedetermination unit 8.

The mapping conversion unit 6 performs SOM processing on the normalizedfeature vector data D3 using the combining weights data wij_fixedbetween nodes in the input layer and neurons in the output layer, whichis determined by the SOM processing unit 43. The mapping conversion unit6 then transmits the SOM output data D_som obtained through the SOMprocessing to the matching processing unit 7.

In Walking state, the SOM output data shown in the right lower portionof FIG. 12 is obtained, for example. In Running state, the SOM outputdata shown in the right lower portion of FIG. 13 is obtained.

The TP data obtaining unit 71 of the matching processing unit 7 reads,from the template storage unit 46, six pieces of template data Tmpl(1)to Tmpl(6) for six states.

The TP data obtaining unit 71 then transmits the six read pieces oftemplate data Tmpl(1) to Tmpl(6) to the first inner product calculationunit 721 to the sixth inner product calculation unit 726, respectively.Note that a case (a case of M=6) in which the matching processing unit 7shown in FIG. 5 includes six inner product calculation units will bedescribed.

The first inner product calculation unit 721 performs processing forcalculating an inner product using the SOM output data D_som and thetemplate data Tmpl(1).

More specifically, the first inner product calculation unit 721 performsprocessing corresponding to the formula below to calculate an innerproduct.

$\begin{matrix}{{Formula}6} & \end{matrix}$ $\begin{matrix}{{{D\_ f}(1)} = {\sum\limits_{j = 1}^{m2}{\sum\limits_{i = 1}^{m1}\left( {D_{ij} \times g_{ij}} \right)}}} & (6)\end{matrix}$

Note that D_(ij) is an SOM output value at the coordinate (i, j) on thesecond-dimensional SOM, and g_(ij) is a value at the coordinate (i, j)of the template data Tmpl(1).

The first inner product calculation unit 721 transmits the signalindicating the calculated inner product value through theabove-described processing to the state determination unit 8 as thesignal D_f(1).

The k-th (k is a natural number satisfying 1≤k≤6) inner productcalculation unit 72 k performs processing for calculating an innerproduct using the SOM output data D_som and the template data Tmpl(k).

More specifically, the k-th inner product calculation unit 72 k performsprocessing corresponding to the formula below to calculate an innerproduct.

$\begin{matrix}{{Formula}7} & \end{matrix}$ $\begin{matrix}{{{D\_ f}(k)} = {\sum\limits_{j = 1}^{m2}{\sum\limits_{i = 1}^{m1}\left( {D_{ij} \times g_{ij}} \right)}}} & (7)\end{matrix}$

Note that D_(ij) is an SOM output value at the coordinate (i, j) on thesecond-dimensional SOM, and g_(ij) is a value at the coordinate (i, j)of the template data Tmpl(k).

The k-th inner product calculation unit 72 k transmits the signalindicating the calculated inner product value through theabove-described processing to the state determination unit 8 as thesignal D_f(k).

The matching processing unit 7 transmits the data obtained through theabove-described processing to the state determination unit 8 as vectordata vec_D_f(=(D_f(1), D_f(2), D_f(3), D_f(4), D_f(5), D_f(6))) foradaptability data.

The state determination unit 8 obtains the vector data vec_D_deci forstate evaluation values based on the received vector data vec_D_acttransmitted from the activity value obtaining unit 5 and the receivedvector data vec_D_f for adaptability data transmitted from the matchingprocessing unit 7.

More specifically, assuming that a state evaluation for State k isD_deci(k) (k is a natural number satisfying 1≤k≤6), the k-thdetermination unit 8 k of the state determination unit 8 obtains a stateevaluation value D_deci(k) for state K through processing correspondingto the following formula:D_deci(k)=D_act(k)×D_f(k).

When the adaptability data is obtained by calculation of the innerproduct as described above, the larger the adaptability data (the innerproduct value) is, the higher the probability that the current state isthe state corresponding to the template used in the processing forcalculating the inner product is.

The larger the activity value D_act(k) is, the higher the probabilitythat data inputted into the state determination apparatus 1000 is dataobtained in State k.

Thus, the larger the state evaluation value D_deci_(k) is, the higherthe probability that a state of data inputted into the statedetermination apparatus 1000 is State k.

The state evaluation values D_deci(1) to D_deci(6) for State 1 to State6 obtained as described above are transmitted, as vector datavec_D_deci, from the state determination unit 8 to the time seriesestimation unit 9.

Using the state transition probability data D_tr_prb, the PF processingunit 91 of the time series estimation unit 9 performs particle filterprocessing on the vector data vec_D_deci to obtain particle filteroutput values (state estimation values) D_pf(1) to D_pf(6) for State 1to State 6.

The specific processing in the PF processing unit 91 will now bedescribed.

First, the state transition probability D_tr_prb will be described.

FIG. 15 is a table showing state transition probabilities for States 1to 6.

As understood from FIG. 15 , for example, a transition probability fromthe state S1 (Sitting) at timing t to the state S2 (Standing) at thetiming t+1 is “0.010”. As understood from FIG. 15 , for example, atransition probability from the state S1 (Sitting) at timing t to thestate S1 (Sitting) at the timing t+1 is “0.990”. In other words, thestate transition probability data D_tr_prb represents probability oftransition from a state at the timing t to a state at the timing t+1 (orprobability that the current state remains during a period from thetiming t to the timing t+1).

The state transition probability data D_tr_prb is stored in a storageunit (not shown) included in the state determination apparatus 1000, andthe control unit reads data from the storage unit and transmits the datato the PF processing unit 91 of the time series estimation unit 9.

FIG. 16 is a state transition diagram for States 1 to 6.

The state transition diagram in FIG. 16 is depicted based on the statetransition probability data D_tr_prb. For example, a transitionprobability from the state S1 to the state S3 is “0”, which is derivedfrom the state transition probability data D_tr_prb, and thus no pathfrom the state S1 to the state S3 exists in the state transition diagramin FIG. 16 .

Particle filter processing performed by the PF processing unit 91 at thetiming t in a case in which the current timing is the timing t and astate at the previous timing t−1 is determined to be “State 4” (State 4(Walking)) will now be described with reference to the drawings.

FIGS. 17 to 19 are state transition diagrams when the state at theprevious timing t−1 is “State 4” (State 4 (Walking)).

FIG. 20 is a diagram describing particle filter processing.

For ease of explanation, it is assumed that the number of particles tobe used in the particle filter processing is “5000” and the valuesD_deci(1) to D_deci(6) obtained by the state determination unit 8 at thetiming t (current timing) are as follows:

-   -   D_deci(1)=0.0    -   D_deci(2)=0.0    -   D_deci(3)=0.0    -   D_deci(4)=0.5    -   D_deci(5)=0.4    -   D_deci(6)=0.1

It is also assumed that the number pre_Pt(k) of particles assigned forthe previous state k, which is a state before the particle filterprocessing at the present timing t is performed, is as follows:

-   -   pre_Pt(1)=250    -   pre_Pt(2)=250    -   pre_Pt(3)=3000    -   pre_Pt(4)=1000    -   pre_Pt(5)=250    -   pre_Pt(6)=250

It is preferable that in an initial processing or a reset processing,the number of particles assigned for each state is the same. Forexample, assuming that the total number of particles to be assigned isP_all, it is preferable that the number of particles assigned for eachof State 1 to State M in the initial processing or the reset processingis “P_all/M”.

The PF processing unit 91 performs prediction processing using the statetransition probability data D_tr_prb.

More specifically, the state at the previous timing t−1 is “State 4”(State 4 (Walking)), and thus the PF processing unit 91 obtainstransition probabilities from State to one of States 1 to 6 based on thestate transition probability data D_tr_prb as follows:

-   -   (Transition probability from State S4 to State S1)=0.000    -   (Transition probability from State S4 to State S2)=0.006    -   (Transition probability from State S4 to State S3)=0.002    -   (Transition probability from State S4 to State S4)=0.990    -   (Transition probability from State S4 to State S5)=0.001    -   (Transition probability from State S4 to State S6)=0.001

Based on the above transition probability, the PF processing unit 91then calculates the number of particles to be moved from the state S4 toindividual states as follows:(The number of particles to be moved from State S4 to State S1)=0×3000=0(The number of particles to be moved from State S4 to StateS2)=0.006×3000=18(The number of particles to be moved from State S4 to StateS3)=0.002×3000=6(The number of particles to remain in State S4)=0.990×3000=2970(The number of particles to be moved from State S4 to StateS5)=0.001×3000=3(The number of particles to be moved from State S4 to StateS6)=0.001×3000=3

Note that the number of particles assigned for the state S4 at theprevious timing t−1 is “600”.

Based on the above, the PF processing unit 91 performs predictionprocessing by moving the above-identified number of particles, and thenobtains the number of particles for each state after the predictionprocessing as follows:Pt(1)=250+0=250Pt(2)=250+18=268Pt(3)=250+6=256Pt(4)=2970Pt(5)=1000+3=1003Pt(6)=250+3=253

Note that the number of particles for the state k after the predictionprocessing is referred to as Pt(k). Next, the PF processing unit 91performs weight calculation processing (likelihood calculationprocessing).

More specifically, the PF processing unit 91 performs weight calculationprocessing (likelihood calculation processing) by obtaining the stateestimation value D_pf(k) for State k through processing corresponding toD_pf(k)=D_deci(k)×Pt(k).

In the above case, the PF processing unit 91 obtains the stateestimation value D_pf(k) for State k as follows:D_pf(1)=D_deci(1)×Pt(1)=0×250=0D_pf(2)=D_deci(2)×Pt(2)=0×268=0D_pf(3)=D_deci(3)×Pt(3)=0×256=0D_pf(4)=D_deci(4)×Pt(4)=0.5×2970=1485D_pf(5)=D_deci(5)×Pt(5)=0.4×1003=401.2D_pf(6)=D_deci(6)×Pt(6)=0.1×253=25.3

The PF processing unit 91 transmits the state estimation values D_pf(1)to D_pf(6) obtained through the above-described processing to the stateestimation unit 92.

The PF processing unit 91 also performs resampling processing.

More specifically, the PF processing unit 91 calculates a total valueSum for D_pf(1) to D_pf(6), and then assigns Pnum (In the presentembodiment, Pnum=5000) particles used for particle filter processing toeach state. In other words, assuming that the number of particles to beassigned for State k after the resampling processing is post_Pt(k),post_P(k) is obtained as follows:

-   -   Pnum=5000    -   Sum=1911.5        post_Pt(1)=Pnum×0/Sum=0        post_Pt(2)=Pnum×0/Sum=0        post_Pt(3)=Pnum×0/Sum=0        post_Pt(4)=Pnum×1485/Sum=3884        post_Pt(5)=Pnum×401.2/Sum=1049        post_Pt(6)=Pnum×25.30/Sum=66

In the above, the values post_Pk(k) are round off to integers. Thenumber of particles assigned for each state obtained through theabove-described processing are set to be the number of particlesassigned for State k before the particle filter processing at the nexttiming t+1 are performed.

At the timing t+1, the PF processing unit 91 performs the sameprocessing as the above-described processing (processing at the timingt).

Note that the PF processing unit 91 stores information on a state (thisstate is referred to as State k_t) in which the number post_Pt(k) ofparticles assigned, at the timing t, for State k after the resamplingprocessing is maximum. At the next timing t+1, the PF processing unit 91performs prediction processing based on the state transition processingdata D_tr_prb assuming that the state at the previous timing t is thestate k_t.

For example, in the above case, a state in which the number post_Pt(k)of particles assigned, at the timing t, for State k after the resamplingprocessing is maximum is State 4 (State 4 (Walking)), and thus the PFprocessing unit 91 performs prediction processing at the next timing t+1based on the state transition processing data D_tr_prb assuming that thestate at the previous timing t is State 4 (State S4).

Note that FIG. 20 shows values calculated in the above processing(processing at the timing t) in the form of a table.

The state estimation unit 92 determines that State k whose stateestimation value D_pf(k) is the largest of the state estimation valuesD_pf(1) to D_pf(6) for State 1 to 6 is a state at current timing t, andthen transmits a signal indicating the determination result, as thesignal Dout, out of the state determination apparatus 1000.

More specifically, (1) when the state estimation value D_pf(1) is thelargest of the state estimation values D_pf(1) to D_pf(6), the stateestimation unit 92 transmits the signal Dout indicating that datainputted into the state determination apparatus 1000 is data obtained inState 1, or “Sitting state” out of the state determination apparatus1000.

(2) when the state estimation value D_pf(2) is the largest of the stateestimation values D_pf(1) to D_pf(6), the state estimation unit 92transmits the signal Dout indicating that data inputted into the statedetermination apparatus 1000 is data obtained in State 2, or “Standingstate” out of the state determination apparatus 1000.(3) when the state estimation value D_pf(3) is the largest of the stateestimation values D_pf(1) to D_pf(6), the state estimation unit 92transmits the signal Dout indicating that data inputted into the statedetermination apparatus 1000 is data obtained in State 3, or “Runningstate” out of the state determination apparatus 1000.(4) when the state estimation value D_pf(4) is the largest of the stateestimation values D_pf(1) to D_pf (6), the state estimation unit 92transmits the signal Dout indicating that data inputted into the statedetermination apparatus 1000 is data obtained in State 4, or “Walkingstate” out of the state determination apparatus 1000.(5) when the state estimation value D_pf(5) is the largest of the stateestimation values D_pf(1) to D_pf(6), the state estimation unit 92transmits the signal Dout indicating that data inputted into the statedetermination apparatus 1000 is data obtained in State 5, or “UpStairsstate” out of the state determination apparatus 1000.(6) when the state estimation value D_pf(6) is the largest of the stateestimation values D_pf(1) to D_pf(6), the state estimation unit 92transmits the signal Dout indicating that data inputted into the statedetermination apparatus 1000 is data obtained in State 6, or “DownStairsstate” out of the state determination apparatus 1000.

For example, in the case of FIGS. 12 and 17 ,D_pf(4)(=Pt(4)×D_deci(4)=297) is maximum as shown in FIG. 20 , and thusthe state estimation unit 92 transmits the signal Dout indicating thatdata inputted into the state determination apparatus 1000 is dataobtained in State 4, or “Walking state” out of the state determinationapparatus 1000.

This allows the state determination apparatus 1000 to appropriatelydetermine that data inputted into the state determination apparatus 1000is data obtained in “Walking state”.

As described above, the state determination apparatus 1000 createstemplate data indicating a state, and calculates a value D_f(k) (e.g.,inner product value) indicating the correlation degree between templatedata and SOM output data. Furthermore, the state determination apparatus1000 obtains norm data of the feature vector data D2, and obtains aprobability density (relative likelihood) by the probability densityfunction using the obtained norm data. In other words, the statedetermination apparatus 1000 obtains, as the activity value D_act(k),the probability density (relative likelihood) for State K, which is astate in obtaining the norm data, from the obtained norm data.

The state determination apparatus 1000 then obtains the state evaluationvalue D_deci(k) based on (1) the value indicating the correlation degreefor State k and (2) the activity value D_act(k) for State k.

The state determination apparatus 1000 obtains transition probabilitybetween states from the state transition probability data D_tr_prb, andthen performs prediction processing of particle filter processing basedon the obtained transition probability to obtain the number Pt(k) ofparticles for State k after prediction processing.

Furthermore, the state determination apparatus 1000 obtains the stateestimation value D_pf(k) based on the number Pt(k) of particles forState k after prediction processing and the state evaluation valueD_deci(k) for State k.

The state estimation value D_pf(k) obtained as described above is basedon the result of time series estimation based on the state transitionprobability between states. Thus, the state determination apparatus 1000performs state determination processing with high accuracy using thestate estimation value D_pf(k).

For example, when two state evaluation values of the state evaluationvalues D_deci(k) at the timing t are close to each other (e.g., whenD_deci(4)=0.5 and D_deci(5)=0.4), the state determination apparatus 1000performs particle filter processing as described above to estimate statetransition in time series. This prevents erroneous determination fromoccurring and allows the state determination apparatus 1000 toappropriately perform state determination processing.

The state determination apparatus 1000 appropriately detects thecorrelation degree (the degree of similarity) between the templateindicating a state and the SOM output data even when a map generated bythe SOM technique includes discontinuous image regions (e.g., splitimage regions). Thus, the state determination apparatus 1000appropriately performs pattern classification processing and/or patterndetermination processing even when a map generated by the SOM techniqueincludes discontinuous image regions (e.g., split image regions).

Furthermore, the state determination apparatus 1000 performs using thestate 16 evaluation value D_deci(k) in consideration of the activityvalue D_ack(k) for State k and performs time series estimationprocessing with particle filter processing in consideration of statetransition probabilities between states, thus allowing for determiningthe state of the input data appropriately. Accordingly, the statedetermination apparatus 1000 performs state determination processingwith high accuracy even when a plurality of pieces of similar templatedata are used.

First Modification

A first modification of the first embodiment will now be described.

The components in this modification that are the same as the componentsdescribed in the above embodiment will be given the same referencenumerals as those components and will not be described in detail.

FIG. 21 is a schematic diagram of a state determination apparatus 1000Aaccording to the first modification of the first embodiment.

FIG. 22 is a schematic diagram of a matching processing unit 7Aaccording to the first modification of the first embodiment.

FIG. 23 is a schematic diagram of a state determination unit 8Aaccording to the first modification of the first embodiment.

As shown in FIG. 21 , the state determination apparatus 1000A of thepresent modification includes a matching processing unit 7A, whichreplaces the matching processing unit 7 of the state determinationapparatus 1000 according to the first embodiment, and a statedetermination unit 8A, which replaces the state determination unit 8 ofthe state determination apparatus 1000 according to the firstembodiment.

The matching processing unit 7A of the present modification includes afirst distance calculation unit 731 to an M-th distance calculation unit73M, which replaces the first inner product calculation unit 721 to theM-th inner product calculation unit 72M, respectively.

The matching processing unit 7 obtains the adaptability data D_f bycalculating an inner product, whereas the matching processing unit 7A ofthe present modification obtains the adaptability data D_f bycalculating a Euclidean distance.

More specifically, the first distance calculation unit 731 performsprocessing corresponding to the formula below to calculate a Euclideandistance between the SOM output data D_som and the template dataTmpl(1).

$\begin{matrix}{{Formula}8} & \end{matrix}$ $\begin{matrix}{{{D\_ f}(1)} = {\sum\limits_{j = 1}^{m2}{\sum\limits_{i = 1}^{m1}{❘{D_{ij} - g_{ij}}❘}^{2}}}} & (8)\end{matrix}$

Note that D_(ij) is an SOM output value at the coordinate (i, j) on thesecond-dimensional SOM, and g_(ij) is a value at the coordinate (i, j)of the template data Tmpl(1).

More specifically, the first distance calculation unit 83 k (k is anatural number satisfying 1≤k≤6) performs processing corresponding tothe formula below to calculate a Euclidean distance between the SOMoutput data D_som and the template data Tmpl(k).

$\begin{matrix}{{Formula}9} & \end{matrix}$ $\begin{matrix}{{{D\_ f}(k)} = {\sum\limits_{j = 1}^{m2}{\sum\limits_{i = 1}^{m1}{❘{D_{ij} - g_{ij}}❘}^{2}}}} & (9)\end{matrix}$

Note that D_(ij) is an SOM output value at the coordinate (i, j) on thesecond-dimensional SOM, and g_(ij) is a value at the coordinate (i, j)of the template data Tmpl(k).

The matching processing unit 7A transmits the data obtained through theabove-described processing to the state determination unit 8A as vectordata vec_D_f(=(D_f(1), D_f(2), D_f(3), D_f(4), D_f(5), D_f(6))) foradaptability data.

As shown in FIG. 23 , the state determination unit 8A includes a firstdetermination unit 81A to a sixth determination unit 86A, whichrespectively replace the first determination unit 81 to the sixthdetermination unit 86.

The state determination unit 8A obtains vector data vec_D_deci for thestate evaluation value based on the vector data vec_D_act transmittedfrom the activity value obtaining unit 5 and the vector data vec_D_f foradaptability data transmitted from the matching processing unit 7A.

More specifically, assuming that a state evaluation value is D_deci(k)(k is a natural number satisfying 1≤k≤6), the k-th determination unit 8kA obtains a state evaluation value D_deci(k) for state K throughprocessing corresponding to the following formula:D_deci(k)=D_act(k)×F1(D_f(k))

Assume that the function F1(x) is a monotonically decreasing functionwith respect to x, and the value of the function F1(x) is alwayspositive value or zero.

When the adaptability data is obtained by calculation of the Euclideandistance as described above, the smaller the adaptability data is, thehigher the probability that the current state is the state correspondingto the template used in the processing for calculating the Euclideandistance. Since the function F1(x) is a monotonically decreasingfunction with respect to x, the larger the value of the functionF1(D_f(k)) is, the higher the probability that the data inputted intothe state determination apparatus 1000A is data in the statecorresponding to the template used in the processing for calculating theEuclidean distance.

The larger the activity value D_act(k) is, the higher the probabilitythat data inputted into the state determination apparatus 1000A is dataobtained in State k.

Thus, the larger the state evaluation value D_deci(k) is, the higher theprobability that data inputted into the state determination apparatus1000A is obtained in State k.

The state evaluation value D_deci(1) to D_deci(6) for State 1 to State 6obtained as described above are transmitted, as vector data vec_D_deci,from the state determination unit 8A to time series estimation unit 9.

Processing performed in the time series estimation unit 9 is the same asone described in the first embodiment.

As described above, the state determination apparatus 1000A obtains theadaptability data D_f(k) for State k using the Euclidean distance, andthen obtains the state evaluation value D_deci(k) based on the obtainedadaptability data D_f(k) for State k and activity value D_act(k) forState k.

The state determination apparatus 1000A obtains state transitionprobabilities between states from the state transition probability dataD_tr_prb, and then performs prediction processing of the particle filterprocessing based on the obtained transition probability to obtain thenumber Pt(k) of particles for State k after the prediction processing.

Furthermore, the state determination apparatus 1000A obtains the stateestimation value D_pf(k) based on the number Pt(k) of particles forState k after the prediction processing and the state evaluation valueD_deci(k) for State k.

The state estimation value D_pf(k) obtained as described above is basedon the result of time series estimation based on the state transitionprobability between states. Thus, the state determination apparatus1000A performs state determination processing with high accuracy usingthe state estimation value D_pf(k).

For example, when two state evaluation values of the state evaluationvalues D_deci(k) at the timing t are close to each other (e.g., whenD_deci(4)=0.5 and D_deci(5)=0.4), the state determination apparatus1000A performs particle filter processing as described above to estimatestate transition in time series. This prevents erroneous determinationfrom occurring and allows the state determination apparatus 1000A toappropriately perform state determination processing.

The state determination apparatus 1000A appropriately detects thecorrelation degree (the degree of similarity) between the templateindicating a state and the SOM output data even when a map generated bythe SOM technique includes discontinuous image regions (e.g., splitimage regions). Thus, the state determination apparatus 1000Aappropriately performs pattern classification processing and/or patterndetermination processing even when a map generated by the SOM techniqueincludes discontinuous image regions (e.g., split image regions).

Furthermore, the state determination apparatus 1000A performs using thestate evaluation value D_deci(k) in consideration of the activity valueD_ack(k) for State k and performs time series estimation processing withparticle filter processing in consideration of state transitionprobabilities between states, thus allowing for determining the state ofthe input data appropriately. This enables the state determinationapparatus 1000A to perform state determination processing with highaccuracy even when a plurality of pieces of similar template data areused.

Second Modification

A second modification of the first embodiment will now be described.

The components in this modification that are the same as the componentsdescribed in the above embodiment will be given the same referencenumerals as those components and will not be described in detail.

FIG. 24 is a schematic diagram of a state determination apparatus 1000Baccording to the second modification of the first embodiment.

FIG. 25 is a schematic diagram of a state determination unit 8Baccording to the second modification of the first embodiment.

As shown in FIG. 24 , the state determination apparatus 1000B accordingto the present modification has the same structure as the statedetermination apparatus 1000 except that it eliminates the activityvalue obtaining unit 5 and replaces the state determination unit 8 witha state determination unit 8B.

As shown in FIG. 24 , the state determination unit 8B includes a firstdetermination unit 81B to a sixth determination unit 86B, which replacesthe first determination unit 81B to the sixth determination unit 86B,respectively.

The state determination unit 8B obtains vector data vec_D_deci for thestate evaluation value based on the vector data vec_D_f for theadaptability data transmitted from the matching processing unit 7A.

More specifically, assuming that a state evaluation value for State k isD_deci(k) (k is a natural number satisfying 1≤k≤6), the k-thdetermination unit 8 kB of the state determination unit 8B obtains thestate evaluation value D_deci(k) for State k as D_deci(k)=D_f(k).

The state evaluation values D_deci(1) to D_deci(6) for State 1 to State6 obtained as described above are transmitted, as vector datavec_D_deci, from the state determination unit 8B to time seriesestimation unit 9.

Processing performed in the time series estimation unit 9 is the same asone described in the first embodiment.

In the state determination apparatus 1000B according to the presentmodification, the matching processing unit 7 may be replaced with thematching processing unit 7A according to the first modification.

In this case, the state determination unit 8B performs as describedbelow.

More specifically, assuming that a state evaluation value for State k isD_deci(k) (k is a natural number satisfying 1≤k≤6), the k-thdetermination unit 8 kB of the state determination unit 8B obtains thestate evaluation value D_deci(k) for State k through processingcorresponding to D_deci(k)=F1(D_f(k)).

Note that the function F1(x) is a monotonically increasing function withrespect to a variable x, and all possible values that the function F1(x)returns are positive values including zero.

When the adaptability data is obtained by calculation of the Euclideandistance as described above, the smaller the adaptability data is, thehigher the probability that the current state is the state correspondingto the template used in the processing for calculating the Euclideandistance. Since the function F1(x) is a monotonically decreasingfunction with respect to x, the larger the value of the functionF1(D_f(k)) is, the higher the probability that the data inputted intothe state determination apparatus 1000B is data in the statecorresponding to the template used in the processing for calculating theEuclidean distance.

Thus, the larger the state determination value D_deci(k) is, the higherthe probability that the data inputted into the state determinationapparatus 1000B is obtained in State k.

The state evaluation values D_deci(1) to D_deci(6) for State 1 to State6 obtained as described above are transmitted, as vector datavec_D_deci, from the state determination unit 8B to time seriesestimation unit 9.

Processing performed in the time series estimation unit 9 is the same asone described in the first embodiment.

As described above, the state determination apparatus 1000B obtains theadaptability data D_f(k) for State k using the result from the innerproduct calculation processing or Euclidean distance calculationprocessing, and then obtains the state evaluation value D_deci(k) basedon the obtained adaptability data D_f(k) for State k.

The state determination apparatus 1000B obtains transition probabilitybetween states from the state transition probability data D_tr_prb, andthen performs prediction processing of particle filter processing basedon the obtained transition probability to obtain the number Pt(k) ofparticles for State k after prediction processing.

Furthermore, the state determination apparatus 1000B obtains the stateestimation value D_pf(k) based on the number Pt(k) of particles forState k after prediction processing and the state evaluation valueD_deci(k) for State k.

The state estimation value D_pf(k) obtained as described above is basedon the result of time series estimation based on the state transitionprobability between states. Thus, the state determination apparatus1000B performs state determination processing with high accuracy usingthe state estimation value D_pf(k).

For example, when two state evaluation values of the state evaluationvalues D_deci(k) at the timing t are close to each other (e.g., whenD_deci(4)=0.5 and D_deci(5)=0.4), the state determination apparatus1000B performs particle filter processing as described above to estimatestate transition in time series. This prevents erroneous determinationfrom occurring and allows the state determination apparatus 1000B toappropriately perform state determination processing.

The state determination apparatus 1000B appropriately detects thecorrelation degree (the degree of similarity) between the templateindicating a state and the SOM output data even when a map generate bythe SOM technique includes discontinuous image regions (e.g., splitimage regions). Thus, the state determination apparatus 1000Bappropriately performs pattern classification processing and/or patterndetermination processing even when a map generated by the SOM techniqueincludes discontinuous image regions (e.g., split image regions).

Other Embodiments

The above embodiments and modifications may be combined to form statedetermination apparatuses.

Although the above embodiments and modifications describe the case inwhich the norm data storage unit 42, the SOM data storage unit 44 andthe template data storage unit 46 are achieved by using differentmemories, the embodiments and modifications should not be limited tothis structure. For example, the norm data storage unit 42, the SOM datastorage unit 44 and the template data storage unit 46 may be achieved byusing one memory.

Although the above embodiments and modifications describe the case inwhich the activity value obtaining unit 5 calculates the activity valueD_act(k) using a probability density function f(x) following the normaldistribution assuming that the norm data of the feature vector data D2for State k follows the normal distribution, the embodiments andmodifications should not be limited to this configuration. For example,the activity value obtaining unit 5 may calculate the value of theprobability density function f(x) using polygonal line approximation orthe like, and then obtain the activity value D_act(k) based on thecalculated approximate value of the probability density function f(x).

Although the above embodiments and modifications describe the case inwhich in the learning phase, the norm obtaining unit 41 obtains normdata and then stores the obtained norm data in the norm data storageunit 42, the embodiments and modifications should not be limited to thisconfiguration. For example, in the template creating phase, the normobtaining unit 41 may obtain norm data and then store the obtained normdata in the norm data storage unit 42.

Although the above embodiments and modifications describe the case inwhich the number of dimension for the feature vector data D2 and thenormalized feature vector data D3 is eight (i.e., in the case ofeight-dimensional vector data), the embodiments and modifications shouldnot be limited to this configuration; the number of the feature vectordata D2 or the normalized feature vector data D3 may be another number.For example, the feature vector data D2 and the normalized featurevector data D3 may be multidimensional vector data other thaneight-dimensional vector data.

The state transition probability data (e.g., the state transitionprobability data shown in FIG. 15 ) described in the above embodimentsand modifications is merely one example; the present invention shouldnot be limited to this disclosure.

Each block of the state determination apparatus described in the aboveembodiments may be formed using a single chip with a semiconductordevice, such as an LSI (large-scale integration) device, or some or allof the blocks of the moving object controller may be formed using asingle chip.

Although LSI is used as the semiconductor device technology, thetechnology may be an integrated circuit (IC), a system LSI, a super LSI,or an ultra LSI depending on the degree of integration of the circuit.

The circuit integration technology employed should not be limited toLSI, but the circuit integration may be achieved using a dedicatedcircuit or a general-purpose processor. A field programmable gate array(FPGA), which is an LSI circuit programmable after manufactured, or areconfigurable processor, which is an LSI circuit in which internalcircuit cells are reconfigurable or more specifically the internalcircuit cells can be reconnected or reset, may be used.

All or part of the processes performed by the functional blocksdescribed in the above embodiments may be implemented by a centralprocessing unit (CPU) in a computer.

All or part of the processes performed by the functional blocksdescribed in the above embodiments may be implemented by a centralprocessing unit (CPU) in a computer.

The programs for these processes may be stored in a storage device, suchas a hard disk or a ROM, and may be executed from the ROM or be readinto a RAM and then executed.

The processes described in the above embodiments may be implemented byusing either hardware or software (including use of an operating system(OS), middleware, or a library), or may be implemented using bothsoftware and hardware.

For example, when functional units of the above embodiments andmodifications is achieved by using software, the hardware structure (thehardware structure including CPU, ROM, RAM, an input unit, an outputunit or the like, each of which is connected to a bus) shown in FIG. 26may be employed to achieve the functional units by using software.

The processes described in the above embodiments may not be performed inthe order specified in the above embodiments. The order in which theprocesses are performed may be changed without departing from the scopeand the spirit of the invention.

The present invention may also include a computer program enabling acomputer to implement the method described in the above embodiments anda computer readable recording medium on which such a program isrecorded. The computer readable recording medium may be, for example, aflexible disk, a hard disk, a CD-ROM, an MO, a DVD, a DVD-ROM, aDVD-RAM, a large-capacity DVD, a next-generation DVD, or a semiconductormemory.

The computer program may not be recorded on the recording medium but maybe transmitted with an electric communication line, a radio or cablecommunication line, or a network such as the Internet.

The term “unit” herein may include “circuitry,” which may be partly orentirely implemented by using either hardware or software, or bothhardware and software.

The specific structures described in the above embodiments are mereexamples of the present invention, and may be changed and modifiedvariously without departing from the scope and the spirit of theinvention.

APPENDIXES

The present invention may also be expressed in the following forms.

A first aspect of the present invention provides a state determinationapparatus including feature vector obtaining circuitry, normalizationcircuitry, mapping conversion circuitry, matching processing circuitry,state determination circuitry, and time series estimation circuitry.

The feature vector obtaining circuitry is configured to obtain featurevector data from measured data obtained by measuring an event with anunknown state.

The normalization circuitry is configured to obtain a norm of thefeature vector data and obtain normalized feature vector data bynormalizing the feature vector data.

The mapping conversion circuitry is configured to obtain SOM output databy mapping the feature vector data into a space whose dimension differsfrom a dimension of the feature vector data.

The matching processing circuitry is configured to obtain adaptabilitydata indicating a correlation degree between template data indicating astate and the SOM output data obtained by the mapping conversioncircuitry.

The state determination circuitry is configured to obtain a stateevaluation value based on the adaptability data obtained by the matchingprocessing circuitry.

The time series estimation circuitry is configured to estimate a stateindicated by the measured data based on the state evaluation value andstate transition probability between states.

In the state determination apparatus, the matching processing circuitryobtains the adaptability data indicating the correlation degree betweentemplate data indicating a state and the SOM output data, and the statedetermination circuitry obtains the state evaluation value based on theadaptability data.

In the state determination apparatus, the time series estimationcircuitry estimates a state indicated by the measured data based on thestate evaluation value and state transition probability between states.

The state determination apparatus can estimate state transition in timeseries based on the state transition probability between states usingthe state evaluation value.

This allows the state determination apparatus to perform statedetermination processing with high accuracy.

As described above, the state determination apparatus appropriatelydetects the correlation degree (the degree of similarity) between thetemplate indicating a state and the SOM output data even when a mapgenerated by the SOM technique includes discontinuous image regions(e.g., split image regions). Thus, the state determination apparatusappropriately performs pattern classification processing and/or patterndetermination processing even when a map generated by the SOM techniqueincludes discontinuous image regions (e.g., split image regions).

A second aspect of the invention provides the state determinationapparatus of the first aspect of the invention further includingactivity value obtaining circuitry configured to obtain an activityvalue indicating a probability that the norm obtained by thenormalization circuitry corresponds to a norm of the feature vector dataobtained in a state.

The state determination circuitry obtains the state evaluation valuebased on the adaptability data obtained by the matching processingcircuitry and the activity value obtained by the activity valueobtaining circuitry.

The time series estimation circuitry estimates the state indicated bythe measured data based on the state evaluation value obtained by thestate determination circuitry and the state transition probabilitybetween states.

In the state determination apparatus, the matching processing circuitryobtains the adaptability data indicating the correlation degree betweenthe template data indicating a state and the SOM output data, and thestate determination circuitry obtains the state evaluation value.

In the state determination apparatus, the time series estimationcircuitry estimates a state indicated by the measured data based on thestate evaluation value and state transition probability between states.

The state determination apparatus can estimate state transition in timeseries based on the state transition probability between states usingthe state evaluation value.

This allows the state determination apparatus to perform statedetermination processing with high accuracy.

As described above, the state determination apparatus appropriatelydetects the correlation degree (the degree of similarity) between thetemplate indicating a state and the SOM output data even when a mapgenerated by the SOM technique includes discontinuous image regions(e.g., split image regions). Thus, the state determination apparatusappropriately performs pattern classification processing and/or patterndetermination processing even when a map generated by the SOM techniqueincludes discontinuous image regions (e.g., split image regions).

Furthermore, the state determination apparatus performs using the stateevaluation value in consideration of the activity value indicating aprobability (relative likelihood) of the state and performs time seriesestimation processing (e.g., particle filter processing) inconsideration of state transition probabilities between states, thusallowing for determining the state of the input data appropriately. Thisenables the state determination apparatus to perform state determinationprocessing with high accuracy even when a plurality of pieces of similartemplate data are used.

A third aspect of the invention provides the state determinationapparatus of the second aspect of the invention in which the activityvalue obtaining circuitry obtains the activity value for a k-th state (kis a natural number satisfying 1≤k≤M, and M is a natural number) throughprocessing corresponding to

$\begin{matrix}{{Formula}10} & \end{matrix}$ $\begin{matrix}{{f(x)} = {\frac{1}{\sqrt{2{\pi\sigma}^{2}}}{\exp\left( {- \frac{\left( {x - \mu} \right)^{2}}{2\sigma^{2}}} \right)}}} & (10)\end{matrix}$where μ is an average value of norms of the feature vector data obtainedin the k-th state, σ is a standard deviation, x is a value of the normof the feature vector data, and f(x) is a probability density function.

Thus, the state determination apparatus obtains the activity value usingthe probability density function f(x) assuming that the norm (norm data)of the feature vector obtained in the k-th state (State k) follows thenormal distribution.

A fourth aspect of the invention provides the state determinationapparatus of the second or third aspect of the invention in which thematching processing circuitry obtains M (M is a natural number) piecesof adaptability data D_f(1) to D_f(M), which respectively indicate thecorrelation degree with respect to M pieces of template data Tmpl(1) toTmpl(M).

The activity value obtaining circuitry obtains activity values D_act(1)to D_act(M), which respectively corresponds to a first state to M-thstate.

The state determination circuitry obtains a state evaluation valueD_deci(k) for k-th state based on the adaptability data D_f(k) for thek-th state and the activity value D_act(k) for the k-th state, anddetermines a state indicated by the measured data based on the obtaineddetermination evaluation value D_est(k) for the k-th state.

The time series estimation circuitry performs particle filter processingusing state transition probability data indicating state transitionprobability between states, performs prediction processing of theparticle filter processing based on the state transition probabilitydata, and obtains the number Pt(k) of particles for the k-th state afterthe prediction processing.

The time series estimation circuitry obtains a state estimation valueD_pf(k) for the k-th state based on the number Pt(k) of particles afterthe prediction processing and the state evaluation value D_deci(k) forthe k-th state, and determines a state indicated by the measured databased on the obtained state estimation value D_pf(k) for the k-th state.

This enables the state determination apparatus to determine thecorrelation degree between the SOM output data obtained from the inputdata and M pieces of template data Tmpl(1) to Tmpl(M) using theadaptability data. Thus, the state determination apparatus appropriatelydetermines which state among the M states corresponding to the templatedata Tmpl(1) to Tmpl(M) is close to the state in which the input data isobtained.

In other words, the state determination apparatus appropriately detectsthe correlation degree (the degree of similarity) between the templateindicating a state and the SOM output data even when a map generated bythe SOM technique includes discontinuous image regions (e.g., splitimage regions). Thus, the state determination apparatus appropriatelyperforms pattern classification processing and/or pattern determinationprocessing even when a map generated by the SOM technique includesdiscontinuous image regions (e.g., split image regions).

Furthermore, the state determination apparatus performs using the stateevaluation value in consideration of the activity value indicating aprobability (relative likelihood) of the state and performs time seriesestimation processing (e.g., particle filter processing) inconsideration of state transition probabilities between states, thusallowing for determining the state of the input data appropriately. Thisenables the state determination apparatus to perform state determinationprocessing with high accuracy even when a plurality of pieces of similartemplate data are used.

A fifth aspect of the invention provides the state determinationapparatus of the fourth aspect of the invention in which the matchingprocessing circuitry calculates inner products using M (M is a naturalnumber) pieces of template data and the SOM output data and obtains thecalculated inner products as the adaptability data D_f(1) to D_f(M).

The state determination circuitry obtains the state evaluation valueD_deci(k) for the k-th state through processing corresponding toD_deci(k)=h1(D_f(k),D_act(k))

-   -   h1(x, y): a function of variables x and y.

The time series estimation circuitry obtains the state estimation valueD_pf(k) for the k-th state through processing corresponding toD_pf(k)=h2(D_deci(k),Pt(k))

-   -   h2(x, y): a function of variables x and y.

The time series estimation circuitry determines that a statecorresponding to the largest state estimation value of the stateestimation values D_pf(1) to D_pf(M) is the state indicated by themeasured data.

The state determination apparatus creates template data indicating astate, and calculates a value (e.g., inner product value) indicating thecorrelation degree between template data and SOM output data.Furthermore, the state determination apparatus obtains norm data of thefeature vector data, and obtains a probability density (relativelikelihood) by the probability density function using the obtained normdata. In other words, the state determination apparatus obtains, as theactivity value D_act(k), the probability density (relative likelihood)for the k-th state, which is a state in obtaining the norm data, fromthe obtained norm data.

The state determination apparatus then obtains the state evaluationvalue D_deci(k) based on based on (1) the value indicating thecorrelation degree for k-th state and (2) the activity value D_act(k)for the k-th state.

The state determination apparatus obtains transition probability betweenstates from the state transition probability data, and then performsprediction processing of the particle filter processing based on theobtained transition probability to obtain the number Pt(k) of particlesfor the k-th state after the prediction processing.

Furthermore, the state determination apparatus obtains the stateestimation value D_pf(k) based on the number Pt(k) of particles for thek-th state after the prediction processing and the state evaluationvalue D_deci(k) for the k-th state.

The state estimation value D_pf(k) obtained as described above is basedon the result of time series estimation based on the state transitionprobability between states. Thus, the state determination apparatusperforms state determination processing with high accuracy using thestate estimation value D_pf(k).

It is preferable that the larger the values of the variables x and yare, the larger the value of the function h1(x, y) is.

In one example, the function h1(x, y) is a function defined by h1(x,y)=x×y.

When the function h1(x, y) is set as described above, the stateevaluation value D_deci(k) for the k-th state (State k) is calculatedusing the following formula:D_deci(k)=D_f(k)×D_act(k).

It is preferable that the larger the values of the variables x and yare, the larger the value of the function h2(x, y) is.

In one example, the function h2(x, y) is a function defined by h2(x,y)=x×y.

When the function h2(x, y) is set as described above, the stateestimation value D_pf(k) for the k-th state (State k) is calculatedusing the following formula:D_pf(k)=D_deci(k)×Pt(k).

A sixth aspect of the invention provides the state determinationapparatus of the fourth aspect of the invention in which the matchingprocessing circuitry calculates Euclidean distances using M (M is anatural number) pieces of template data and the SOM output data andobtains the calculated Euclidean distances as the adaptability dataD_f(1) to D_f(M).

The state determination circuitry obtains the state evaluation valueD_deci(k) for the k-th state through processing corresponding toD_deci(k)=h1(F1(D_f(k)),D_act(k))where h1(x,y) is a function of variables x and y, and F1(x) is amonotonically decreasing function with respect to x and the value of thefunction F1(x) is a positive value or zero.

The time series estimation circuitry obtains the state estimation valueD_pf(k) for the k-th state through processing corresponding toD_pf(k)=h2(D_deci(k),Pt(k))

-   -   h2(x, y): a function of variables x and y.

The time series estimation circuitry determines that a statecorresponding to the largest state estimation value of the stateestimation values D_pf(1) to D_pf(M) is the state indicated by themeasured data.

Thus, the state determination apparatus obtains the correlation degreebetween the SOM output data obtained from the input data and the Mpieces of template data Tmpl(1) to Tmpl(M) by calculating the Euclideandistances.

It is preferable that the larger the values of the variables x and yare, the larger the value of the function h1(x, y) is.

In one example, the function h1(x, y) is a function defined by h1(x,y)=x×y.

When the function h1(x, y) is set as described above, the stateevaluation value D_deci(k) for the k-th state (State k) is calculatedusing the following formula:D_deci(k)=F1(D_f(k))×D_act(k).

It is preferable that the larger the values of the variables x and yare, the larger the value of the function h2(x, y) is.

In one example, the function h2(x, y) is a function defined by h2(x,y)=x×y.

When the function h2(x, y) is set as described above, the stateestimation value D_pf(k) for the k-th state (State k) is calculatedusing the following formula:D_pf(k)=D_deci(k)×Pt(k).

A seventh aspect of the invention provides the state determinationapparatus of one of the second to sixth aspect of the invention in whichthe template data is composed of two-dimensional elements, and a sum ofall the values of the elements of the template data is “1”.

In the state determination apparatus, normalization is performed suchthat the sum of the values of element data of the template data is “1”.Such normalization allows possible range of values indicatingcorrelation degree (e.g., inner product values or Euclidean distances)to be constant (common) regardless of templates to be used in thedetermination processing for determining the correlation degree betweentemplate data and SOM output data. This allows classificationdetermination processing to be easily achieved.

An eighth aspect of the invention provides the state determinationapparatus of one of the second to seventh aspect of the invention inwhich the template data is composed of two-dimensional elements.

The template data is created based on N pieces of SOM output dataobtained during a period from timing t=1 to timing t=N (N is a naturalnumber) in which an identical state continues.

Assuming that the template data is composed of m×n (m and n are naturalnumbers) pieces of data, the template data is created by setting a valueg_(ij) to element data at a second-dimensional coordinate (i,j) (i and jare natural numbers satisfying 1≤i≤m and 1≤j≤n) of the template, thevalue g_(ij) being obtained by

$\begin{matrix}{{Formula}11} & \end{matrix}$ $\begin{matrix}{g_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{D_{ij}(t)}}}} & (11)\end{matrix}$where g_(ij) is element data at a second-dimensional coordinate (i, j)(i and j are natural numbers satisfying 1≤i≤m and 1≤j≤n) of the templatedata, and D_(ij)(t) is element data at a second-dimensional coordinate(i, j) of the SOM output data at timing t.

Thus, the state determination apparatus performs state determinationprocessing using the template data obtained by averaging, in thetemporal direction, the SOM output data obtained during the period inwhich the identical state continues.

A ninth aspect of the invention provides the state determinationapparatus of the one of the second to seventh aspect of the invention inwhich the template data is composed of two-dimensional elements.

The template data is created based on N pieces of SOM output dataobtained during a period from timing t=1 to timing t=N (N is a naturalnumber) in which an identical state continues.

Assuming that (1) the template data is composed of m×n (m and n arenatural numbers) pieces of data, (2) g_(ij) is element data at asecond-dimensional coordinate (i, j) (i and j are natural numberssatisfying 1≤i≤m and 1≤j≤n) of the template data, and (3) D_(ij)(t) iselement data at a second-dimensional coordinate (i, j) of the SOM outputdata at timing t, and the template data is created by setting an averagevalue obtained by calculating an average value of the element dataD_(ij)(t) satisfying D_(ij)(t)≥Th1 to a value g_(ij) at asecond-dimensional coordinate (i, j) of the template data.

Thus, the state determination apparatus performs state determinationprocessing using the template data obtained by averaging, in thetemporal direction, the SOM output data obtained during the period inwhich the identical state continues. The template data is createdthrough the averaging processing with the data smaller than thethreshold excluded. Using such template data allows the statedetermination apparatus to perform appropriate state determinationprocessing.

A tenth aspect of the invention provides the state determinationapparatus of one of the second to seventh aspect of the invention inwhich the template data is composed of two-dimensional elements.

The template data is created based on N pieces of SOM output dataobtained during a period from timing t=1 to timing t=N (N is a naturalnumber) in which an identical state continues.

Assuming that (1) the template data is composed of m×n (m and n arenatural numbers) pieces of data, (2) g_(ij) is element data at asecond-dimensional coordinate (i, j) (i and j are natural numberssatisfying 1≤i≤m and 1≤j≤n) of the template data, and (3) D_(ij)(t) iselement data at a second-dimensional coordinate (i, j) of the SOM outputdata at timing t, and the template data is created by setting a valueg_(ij) to element data at a second-dimensional coordinate (i, j) (i andj are natural numbers satisfying 1≤i≤m and 1≤j≤n) of the template, thevalue g_(ij) being obtained by calculating an average value of valuesD_(ij)(t) of the SOM output data at the coordinate (i, j) whosecorresponding variance value v_(ij) is obtained by

$\begin{matrix}{{Formula}12} & \end{matrix}$ $\begin{matrix}{{AveD}_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{D_{ij}(t)}}}} & (12)\end{matrix}$ $\begin{matrix}{{Formula}13} & \end{matrix}$ $\begin{matrix}{v_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}\left( {{D_{ij}(t)} - {AveD}_{ij}} \right)^{2}}}} & (13)\end{matrix}$and satisfies v_(ij)≤Th2.

Thus, the state determination apparatus performs state determinationprocessing using the template data obtained by averaging, in thetemporal direction, the SOM output data obtained during the period inwhich the identical state continues. The template data is createdthrough the averaging processing using only data whose variance in thetemporal direction is smaller than the threshold. Using such templatedata allows the state determination apparatus to perform appropriatestate determination processing.

An eleventh aspect of the invention provides the state determinationapparatus of one of the second to seventh aspect of the invention inwhich the template data is composed of two-dimensional elements.

The template data is created based on N pieces of SOM output dataobtained during a period from timing t=1 to timing t=N (N is a naturalnumber) in which an identical state continues.

Assuming that (1) the template data is composed of m×n (m and n arenatural numbers) pieces of data, (2) g_(ij) is element data at asecond-dimensional coordinate (i, j) (i and j are natural numberssatisfying 1≤i≤m and 1≤j≤n) of the template data, and (3) D_(ij)(t) iselement data at a second-dimensional coordinate (i, j) of the SOM outputdata at timing t, and the template data is created by setting a valueg_(ij) to element data at a second-dimensional coordinate (i, j) (i andj are natural numbers satisfying 1≤i≤m and 1≤j≤n) of the template, thevalue g_(ij) being obtained by calculating an average value of valuesD_(ij)(t) of the SOM output data at the coordinate (i, j) whosecorresponding variance value v_(ij) is obtained by

$\begin{matrix}{{Formula}14} & \end{matrix}$ $\begin{matrix}{{AveD}_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{D_{ij}(t)}}}} & (14)\end{matrix}$ $\begin{matrix}{{Formula}15} & \end{matrix}$ $\begin{matrix}{v_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}\left( {{D_{ij}(t)} - {AveD}_{ij}} \right)^{2}}}} & (15)\end{matrix}$and satisfies v_(ij)≤Th2, the values D_(ij)(t) of the SOM output dataeach satisfying D_(ij)(t)>Th3 where Th3 is a threshold.

Thus, the state determination apparatus performs state determinationprocessing using the template data obtained by averaging, in thetemporal direction, the SOM output data obtained during the period inwhich the identical state continues. The template data is createdthrough the averaging processing using data whose variance in thetemporal direction is smaller than the threshold and element data largerthan the threshold. Using such template data allows the statedetermination apparatus to perform appropriate state determinationprocessing.

A twelfth aspect of the invention provides the state determinationapparatus of the one of the second to seventh aspect of the invention inwhich the template data is composed of two-dimensional elements.

The template data is created based on N pieces of SOM output dataobtained during a period from timing t=1 to timing t=N (N is a naturalnumber) in which an identical state continues.

The template data is created based on N pieces of SOM output dataobtained during a period from timing t=1 to timing t=N (N is a naturalnumber) in which an identical state continues.

Assuming that (1) the template data is composed of m×n (m and n arenatural numbers) pieces of data, (2) gi is element data at asecond-dimensional coordinate (i, j) (i and j are natural numberssatisfying 1≤i≤m and 1≤j≤n) of the template data, (3) D_(ij)(t) iselement data at a second-dimensional coordinate (i, j) of the SOM outputdata at timing t, and (4) σ_(ij) is a standard deviation of element dataat the coordinate (i, j) obtained by

$\begin{matrix}{{Formula}16} & \end{matrix}$ $\begin{matrix}{{AveD}_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{D_{ij}(t)}}}} & (16)\end{matrix}$ $\begin{matrix}{{Formula}17} & \end{matrix}$ $\begin{matrix}{v_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}\left( {{D_{ij}(t)} - {AveD}_{ij}} \right)^{2}}}} & (17)\end{matrix}$σ_(ij)=sqrt(v _(ij))

-   -   sqrt(x): a function that returns the square root of x, the        template data is created by (A) setting an average values of the        SOM output data D_(ij)(t) to the value g_(ij) when        abs(D_(ij)(t)−AveD_(ij))≤n1×σ_(ij) is satisfied and (B) setting        zero to the value g_(ij) when abs(D_(ij)(t)−AveD_(ij))≤n1×σ_(ij)        is not satisfied.

Thus, the state determination apparatus performs state determinationprocessing using the template data obtained by averaging, in thetemporal direction, the SOM output data obtained during the period inwhich the identical state continues. The template data is createdthrough the averaging processing using data whose value is within arange from the average value minus 3σ to the average value plus 3σinclusive. Using such template data allows the state determinationapparatus to perform appropriate state determination processing.

A thirteenth aspect of the present invention provides a statedetermination method including a feature vector obtaining step, anormalization step, a mapping conversion step, a matching processingstep, an activity value obtaining step, a state determination step, anda time series estimation step.

The feature vector obtaining step includes obtaining feature vector datafrom measured data obtained by measuring an event with an unknown state.

The normalization step includes obtaining a norm of the feature vectordata and obtaining normalized feature vector data by normalizing thefeature vector data;

The mapping conversion step includes obtaining SOM output data bymapping the normalized feature vector data into a space whose dimensiondiffers from a dimension of the normalized feature vector data.

The matching processing step includes obtaining adaptability dataindicating a correlation degree between template data indicating a stateand the SOM output data obtained by the mapping conversion step.

The activity value obtaining step includes obtaining an activity valueindicating a probability that the norm obtained by the normalizationstep corresponds to a norm of the feature vector data obtained in astate.

The state determination step including obtaining a state evaluationvalue based on at least one of the adaptability data obtained by thematching processing circuitry and the activity value obtained by theactivity value obtaining step.

The time series estimation step includes estimating a state indicated bythe measured data based on the state evaluation value and statetransition probability between states.

This achieves the state determination method having the sameadvantageous effects as the state determination apparatus of the firstor second aspect of the present invention.

A fourteenth aspect of the present invention provides an integratedcircuit including feature vector obtaining circuitry, normalizationcircuitry, mapping conversion circuitry, matching processing circuitry,activity value obtaining circuitry, state determination circuitry, andtime series estimation circuitry.

The feature vector obtaining circuitry is configured to obtain featurevector data from measured data obtained by measuring an event with anunknown state.

The normalization circuitry is configured to obtain a norm of thefeature vector data and obtain normalized feature vector data bynormalizing the feature vector data.

The mapping conversion circuitry is configured to obtain SOM output databy mapping the feature vector data into a space whose dimension differsfrom the dimension of the feature vector data.

The matching processing circuitry configured to obtain adaptability dataindicating a correlation degree between template data indicating a stateand the SOM output data obtained by the mapping conversion circuitry.

The activity value obtaining circuitry is configured to obtain anactivity value indicating a probability that the norm obtained by thenormalization circuitry corresponds to a norm of the feature vector dataobtained in a state.

The state determination circuitry is configured to obtain a stateevaluation value based on at least one of the adaptability data obtainedby the matching processing circuitry and the activity value obtained bythe activity value obtaining circuitry.

The time series estimation circuitry is configured to estimate a stateindicated by the measured data based on the state evaluation value andstate transition probability between states.

This achieves the integrated circuit having the same advantageouseffects as the state determination apparatus of the first or secondaspect of the present invention.

What is claimed is:
 1. A state determination apparatus comprising:feature vector obtaining circuitry configured to obtain feature vectordata from measured data obtained by measuring an event by a targetobject with an unknown state; normalization circuitry configured toobtain a norm of the feature vector data and obtain normalized featurevector data by normalizing the feature vector data; mapping conversioncircuitry configured to obtain SOM output data by mapping the normalizedfeature vector data into a space whose dimension differs from adimension of the normalized feature vector data using only a single map,the SOM output data being outputted from a last output laver of SOM, theSOM output data being two-dimensional map data including discontinuousimage regions, wherein the mapping conversion circuitry is configured toobtain the SOM output data based on the normalized feature vector dataand SOM combining weight vector data; matching processing circuitryconfigured to obtain adaptability data indicating a correlation degreebetween template data indicating a k-th state, k being a natural numbersatisfying 1≤k≤M, where M is a natural number, and the SOM output data,by calculating an inner product (IP(k)) or a Euclidean distance (ED(k))between the template data and the SOM output, the template data being acalculated average value of the k-th state in a m1×m2 two-dimensionaltemplate data form, m1 and m2 being natural numbers, wherein the IP(k)is calculated by${{IP}(k)} = {\sum\limits_{j = 1}^{m2}{\sum\limits_{i = 1}^{m1}\left( {D_{ij} \times g_{ij}} \right)}}$and the ED(k) is calculated by${E{D(k)}} = {\sum\limits_{j = 1}^{m2}{\sum\limits_{i = 1}^{m1}{❘{D_{ij} - g_{ij}}❘}^{2}}}$where g_(ij) is element data at a second-dimensional coordinate (i, j),i and j being natural numbers satisfying 1≤i≤m1 and 1≤j≤m2 of thetemplate data, and D_(ij) is element data at the second-dimensionalcoordinate (i, j) of the SOM output data; activity value obtainingcircuitry configured to obtain an activity value indicating aprobability that the norm obtained by the normalization circuitrycorresponds to a norm of the feature vector data obtained in a state;state determination circuitry configured to obtain a state evaluationvalue using both the adaptability data obtained by the matchingprocessing circuitry and the activity value obtained by the activityvalue obtaining circuitry, the state evaluation value being obtainedafter the adaptability data and the activity value have been obtained bythe matching processing circuitry and the activity value obtainingcircuitry respectively; and time series estimation circuitry configuredto: obtain information regarding a previous state of the target object,obtain, from a memory, each state transition probability from theprevious state to a plurality of predetermined states, the memorystoring, as the state transition probability, each probability data forcombinations of transitions from one of the plurality of predeterminedstates at a first timing to another of the plurality of predeterminedstates at a second timing after the first timing, and estimate a currentstate of the target object, from among the plurality of predeterminedstates, indicated by the measured data based on the state evaluationvalue and each of the obtained state transition probability from theprevious state to the plurality of predetermined states, by identifying,from among the plurality of predetermined states, a state having alargest value after multiplying the state evaluation value with each ofthe obtained state transition probability from the previous state to theplurality of predetermined states.
 2. The state determination apparatusaccording to claim 1, wherein the activity value obtaining circuitryobtains the activity value for the k-th state through processingcorresponding to $\begin{matrix}{{f(x)} = {\frac{1}{\sqrt{2{\pi\sigma}^{2}}}{\exp\left( {- \frac{\left( {x - \mu} \right)^{2}}{2\sigma^{2}}} \right)}}} & ({cl3})\end{matrix}$ where μ is an average value of norms of the feature vectordata obtained in the k-th state, σ is a standard deviation, x is a valueof the norm of the feature vector data, and f(x) is a probabilitydensity function.
 3. The state determination apparatus according toclaim 1, wherein the matching processing circuitry obtains M (M is anatural number) pieces of adaptability data D_f(1) to D_f(M), whichrespectively indicate the correlation degree with respect to M pieces oftemplate data Tmpl(1) to Tmpl(M), the activity value obtaining circuitryobtains activity values D_act(1) to D_act(M), which respectivelycorresponds to a first state to an M-th state, the state determinationcircuitry obtains a state evaluation value D_deci(k) for the k-th statebased on the adaptability data D_f(k) for the k-th state and theactivity value D_act(k) for the k-th state, the time series estimationcircuitry performs particle filter processing using state transitionprobability data indicating state transition probability between states,performs prediction processing of the particle filter processing basedon the state transition probability data, obtains the number Pt(k) ofparticles for the k-th state after the prediction processing, obtains astate estimation value D_pf(k) for the k-th state based on the numberPt(k) of particles after the prediction processing and the stateevaluation value D_deci(k) for the k-th state, and determines a stateindicated by the measured data based on the obtained state estimationvalue D_pf(k) for the k-th state.
 4. The state determination apparatusaccording to claim 3, wherein the matching processing circuitrycalculates inner products using M (M is a natural number) pieces of thetemplate data and the SOM output data and obtains the calculated innerproducts as the adaptability data D_f(1) to D_f(M), and the statedetermination circuitry obtains the state evaluation value D_deci(k) forthe k-th state through processing corresponding toD_deci(k)=h1(D_f(k),D_act(k)) h1(x, y): a function of variables x and y,and the time series estimation circuitry obtains the state estimationvalue D_pf(k) for the k-th state through processing corresponding toD_pf(k)=h2(D_deci(k),Pt(k)) h2(x, y): a function of variables x and y,and determines that a state corresponding to the largest stateestimation value of the state estimation values D_pf(1) to D_pf(M) isthe state indicated by the measured data.
 5. The state determinationapparatus according to claim 3, wherein the matching processingcircuitry calculates Euclidean distances using M (M is a natural number)pieces of the template data and the SOM output data and obtains thecalculated Euclidean distances as the adaptability data D_f(1) toD_f(M), and the state determination circuitry obtains the stateevaluation value D_deci(k) for the k-th state through processingcorresponding toD_deci(k)=h1(F1(D_f(k)),D_act(k)) where h1(x,y) is a function ofvariables x and y, and F1(x) is a monotonically decreasing function withrespect to x and the value of the function F1(x) is a positive value orzero, and the time series estimation circuitry obtains the stateestimation value D_pf(k) for the k-th state through processingcorresponding toD_pf(k)=h2(D_deci(k),Pt(k)) h2(x, y): a function of variables x and y,and determines that a state corresponding to the largest stateestimation value of the state estimation values D_pf(1) to D_pf(M) isthe state indicated by the measured data.
 6. The state determinationapparatus according to claim 1, wherein the template data is composed oftwo-dimensional elements, and a sum of all values of the two-dimensionalelements is “1”.
 7. The state determination apparatus according to claim1, wherein the template data is composed of two-dimensional elements,the template data is created based on N pieces of SOM output dataobtained during a period from timing t=1 to timing t=N (N is a naturalnumber) in which an identical state continues, and assuming that thetemplate data is composed of m×n (m and n are natural numbers) pieces ofdata, the template data is created by setting a value g_(ij) to elementdata at a second-dimensional coordinate (i, j) (i and j are naturalnumbers satisfying 1≤i≤m and 1≤j≤n) of the template, the value g_(ij)being obtained by $\begin{matrix}{g_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{D_{ij}(t)}}}} & ({cl8})\end{matrix}$ where g_(ij) is element data at a second-dimensionalcoordinate (i, j) (i and j are natural numbers satisfying 1≤i≤m and1≤j≤n) of the template data, and D_(ij)(t) is element data at asecond-dimensional coordinate (i, j) of the SOM output data at timing t.8. The state determination apparatus according to claim 1, wherein thetemplate data is composed of two-dimensional elements, the template datais created based on N pieces of SOM output data obtained during a periodfrom timing t=1 to timing t=N (N is a natural number) in which anidentical state continues, and assuming that (1) the template data iscomposed of m×n (m and n are natural numbers) pieces of data, (2) g_(ij)is element data at a second-dimensional coordinate (i, j) (i and j arenatural numbers satisfying 1≤i≤m and 1≤j≤n) of the template data, and(3) D_(ij)(t) is element data at a second-dimensional coordinate (i, j)of the SOM output data at timing t, and the template data is created bysetting an average value obtained by calculating an average value of theelement data D_(ij)(t) satisfying D_(ij)(t)≥Th1 to a value g_(ij) at asecond-dimensional coordinate (i, j) of the template data.
 9. The statedetermination apparatus according to claim 1, wherein the template datais composed of two-dimensional elements, the template data is createdbased on N pieces of SOM output data obtained during a period fromtiming t=1 to timing t=N (N is a natural number) in which an identicalstate continues, and assuming that (1) the template data is composed ofm×n (m and n are natural numbers) pieces of data, (2) g_(ij) is elementdata at a second-dimensional coordinate (i, j) (i and j are naturalnumbers satisfying 1≤i≤m and 1≤j≤n) of the template data, and (3)D_(ij)(t) is element data at a second-dimensional coordinate (i, j) ofthe SOM output data at timing t, and the template data is created bysetting a value g_(ij) to element data at a second-dimensionalcoordinate (i, j) (i and j are natural numbers satisfying 1≤i≤m and1≤j≤n) of the template, the value g_(ij) being obtained by calculatingan average value of values D_(ij)(t) of the SOM output data at thecoordinate (i, j) whose corresponding variance value v_(ij) is obtainedby $\begin{matrix}{{AveD}_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{D_{ij}(t)}}}} & \left( {{cl10} - 1} \right)\end{matrix}$ $\begin{matrix}{v_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}\left( {{D_{ij}(t)} - {AveD}_{ij}} \right)^{2}}}} & \left( {{cl10} - 2} \right)\end{matrix}$ and satisfies v_(ij)≤Th2.
 10. The state determinationapparatus according to claim 1, wherein the template data is composed oftwo-dimensional elements, the template data is created based on N piecesof SOM output data obtained during a period from timing t=1 to timingt=N (N is a natural number) in which an identical state continues, andassuming that (1) the template data is composed of m×n (m and n arenatural numbers) pieces of data, (2) g_(ij) is element data at asecond-dimensional coordinate (i, j) (i and j are natural numberssatisfying 1≤i≤m and 1≤j≤n) of the template data, and (3) D_(ij)(t) iselement data at a second-dimensional coordinate (i, j) of the SOM outputdata at timing t, and the template data is created by setting a valueg_(ij) to element data at a second-dimensional coordinate (i, j) (i andj are natural numbers satisfying 1≤i≤m and 1≤j≤n) of the template, thevalue g_(ij) being obtained by calculating an average value of valuesD_(ij)(t) of the SOM output data at the coordinate (i, j) whosecorresponding variance value v_(ij) is obtained by $\begin{matrix}{{AveD}_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{D_{ij}(t)}}}} & \left( {{cl}11 - 1} \right)\end{matrix}$ $\begin{matrix}{v_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}\left( {{D_{ij}(t)} - {AveD}_{ij}} \right)^{2}}}} & \left( {{cl11} - 2} \right)\end{matrix}$ and satisfies v_(ij)≤Th2, the values D_(ij)(t) of the SOMoutput data each satisfying D_(ij)(t)>Th3 where Th3 is a threshold. 11.The state determination apparatus according to claim 1, wherein thetemplate data is composed of two-dimensional elements, the template datais created based on N pieces of SOM output data obtained during a periodfrom timing t=1 to timing t=N (N is a natural number) in which anidentical state continues, and assuming that (1) the template data iscomposed of m×n (m and n are natural numbers) pieces of data, (2) g_(ij)is element data at a second-dimensional coordinate (i, j) (i and j arenatural numbers satisfying 1≤i≤m and 1≤j≤n) of the template data, (3)D_(ij)(t) is element data at a second-dimensional coordinate (i, j) ofthe SOM output data at timing t, and (4) σ_(ij) is a standard deviationof element data at the coordinate (i, j) obtained by $\begin{matrix}{{AveD}_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}{D_{ij}(t)}}}} & \left( {{cl2} - 1} \right)\end{matrix}$ $\begin{matrix}{v_{ij} = {\frac{1}{N}{\sum\limits_{t = 1}^{N}\left( {{D_{ij}(t)} - {AveD}_{ij}} \right)^{2}}}} & \left( {{cl}12 - 2} \right)\end{matrix}$σ_(ij)=sqrt(v _(ij)) sqrt(x): a function that returns the square root ofx, the template data is created by (A) setting an average value of theSOM output data D_(ij)(t) to the value g_(ij) whenabs(D_(ij)(t)−AveD_(ij))≤n1×σ_(ij) is satisfied and (B) setting zero tothe value g_(ij) when abs(D_(ij)(t)−AveD_(ij))≤n1×σ_(ij) is notsatisfied.
 12. A state determination method comprising: (A) obtainingfeature vector data from measured data obtained by measuring an event bya target object with an unknown state; (B) obtaining a norm of thefeature vector data and obtaining normalized feature vector data bynormalizing the feature vector data; (C) obtaining SOM output data bymapping the normalized feature vector data into a space whose dimensiondiffers from a dimension of the normalized feature vector data usingonly a single map, the SOM output data being outputted from a lastoutput laver of SOM, the SOM output data being two-dimensional map dataincluding discontinuous image regions, wherein the SOM output data isobtained based on the normalized feature vector data and SOM combiningweight vector data; (D) obtaining adaptability data indicating acorrelation degree between template data indicating a k-th state, kbeing a natural number satisfying 1≤k≤M, where M is a natural number,and the SOM output data, by calculating an inner product (IP(k)) or aEuclidean distance (ED(k)) between the template data and the SOM output,the template data being a calculated average value of the k-th state ina m1×m2 two-dimensional template data form, m1 and m2 being naturalnumbers, wherein the IP(k) is calculated by${{IP}(k)} = {\sum\limits_{j = 1}^{m2}{\sum\limits_{i = 1}^{m1}\left( {D_{ij} \times g_{ij}} \right)}}$and the ED(k) is calculated by${{ED}(k)} = {\sum\limits_{j = 1}^{m2}{\sum\limits_{i = 1}^{m1}{❘{D_{ij} - g_{ij}}❘}^{2}}}$where g_(ij) is element data at a second-dimensional coordinate (i, j),i and j being natural numbers satisfying 1≤i≤m1 and 1≤j≤m2 of thetemplate data, and D_(ij) is element data at the second-dimensionalcoordinate (i, j) of the SOM output data; (E) obtaining an activityvalue indicating a probability that the norm obtained by the step (B)corresponds to a norm of the feature vector data obtained in a state;(F) obtaining a state evaluation value using both the adaptability dataobtained by the step (D) and the activity value obtained by the step(E), the state evaluation value being obtained after the adaptabilitydata and the activity value have been obtained by the step (D) and thestep (E) respectively; (G) obtaining information regarding a previousstate of the target object; (H) obtaining, from a memory, each statetransition probability from the previous state to a plurality ofpredetermined states, the memory storing, as the state transitionprobability, each probability data for combinations of transitions fromone of the plurality of predetermined states at a first timing toanother of the plurality of predetermined states at a second timingafter the first timing; and (I) estimating a current state of the targetobject, from among the plurality of predetermined states, indicated bythe measured data based on the state evaluation value and each of theobtained state transition probability from the previous state to theplurality of predetermined states, by identifying, from among theplurality of predetermined states, a state having a largest value aftermultiplying the state evaluation value with each of the obtained statetransition probability from the previous state to the plurality ofpredetermined states.
 13. An integrated circuit comprising: featurevector obtaining circuitry configured to obtain feature vector data frommeasured data obtained by measuring an event by a target object with anunknown state; normalization circuitry configured to obtain a norm ofthe feature vector data and obtain normalized feature vector data bynormalizing the feature vector data; mapping conversion circuitryconfigured to obtain SOM output data by mapping the normalized featurevector data into a space whose dimension differs from a dimension of thenormalized feature vector data using only a single map, the SOM outputdata being outputted from a last output laver of SOM, the SOM outputdata being two-dimensional map data including discontinuous imageregions, wherein the manning conversion circuitry is configured toobtain the SOM output data based on the normalized feature vector dataand SOM combining weight vector data; matching processing circuitryconfigured to obtain adaptability data indicating a correlation degreebetween template data indicating a k-th state, k being a natural numbersatisfying 1≤k≤M, where M is a natural number, and the SOM output data,by calculating an inner product (IP(k)) or a Euclidean distance (ED(k))between the template data and the SOM output, the template data being acalculated average value of the k-th state in a m1×m2 two-dimensionaltemplate data form, m1 and m2 being natural numbers, wherein the IP(k)is calculated by${{IP}(k)} = {\sum\limits_{j = 1}^{m2}{\sum\limits_{i = 1}^{m1}\left( {D_{ij} \times g_{ij}} \right)}}$and the ED(k) is calculated by${{ED}(k)} = {\sum\limits_{j = 1}^{m2}{\sum\limits_{i = 1}^{m1}{❘{D_{ij} - g_{ij}}❘}^{2}}}$where g_(ij) is element data at a second-dimensional coordinate (i, j),i and j being natural numbers satisfying 1≤i≤m1 and 1≤j≤m2 of thetemplate data, and D_(ij) is element data at the second-dimensionalcoordinate (i, j) of the SOM output data; activity value obtainingcircuitry configured to obtain an activity value indicating aprobability that the norm obtained by the normalization circuitrycorresponds to a norm of the feature vector data obtained in a state;state determination circuitry configured to obtain a state evaluationvalue using both the adaptability data obtained by the matchingprocessing circuitry and the activity value obtained by the activityvalue obtaining circuitry, the state evaluation value being obtainedafter the adaptability data and the activity value have been obtained bythe matching processing circuitry and the activity value obtainingcircuitry respectively; and time series estimation circuitry configuredto: obtain information regarding a previous state of the target object,obtain, from a memory, each state transition probability from theprevious state to a plurality of predetermined states, the memorystoring, as the state transition probability, each probability data forcombinations of transitions from one of the plurality of predeterminedstates at a first timing to another of the plurality of predeterminedstates at a second timing after the first timing, and estimate a currentstate of the target object, from among the plurality of predeterminedstates, indicated by the measured data based on the state evaluationvalue and each of the obtained state transition probability from theprevious state to the plurality of predetermined states, by identifying,from among the plurality of predetermined states, a state having alargest value after multiplying the state evaluation value with each ofthe obtained state transition probability from the previous state to theplurality of predetermined states.
 14. The state determination apparatusaccording to claim 1, wherein the state transition probability is storedin the memory in a table format.
 15. The state determination apparatusaccording to claim 1, wherein the measured data is obtained by at leasta sensor attached to a human body, and the time series estimationcircuitry is configured to estimate a state of the human body, fromamong the plurality of the states including at least a sitting stateindicating the human body is sitting, a standing state indicating thehuman body is standing, a running state indicating the human body isrunning, and a walking state indicating the human body is walking, anupstairs state indicating the human body is going upstairs, and adownstairs state indicating the human body is going down stairs,indicated by the measured data based on the state evaluation value andthe state transition probability between states, the state transitionprobability being stored in a memory and including each probability datafor combinations of transitions from one of the sitting state, thestanding state, the running state, the walking state, the upstairsstate, and the downstairs state at a first timing to another of thesitting state, the standing state, the running state, the walking state,the upstairs state, and the downstairs state at a second timing afterthe first timing.
 16. The state determination apparatus according toclaim 1, wherein the SOM combining weight vector data indicates acombining weight between nodes in an input layer and neurons in anoutput layer in a learning phase of the mapping conversion circuitry.